Complexity indexes from instrumented timed-up-and-go test reveal depression-related movement differences

Complexity indexes from instrumented timed-up-and-go test reveal depression-related movement differences | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Complexity indexes from instrumented timed-up-and-go test reveal depression-related movement differences Giulia Fransvea, Elisabetta Patron, Eleonora Vendrame, Luigi Truppa, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4174631/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Major Depressive Disorder (MDD) is one of the most common psychological disorders. The multiplicity of its clinical patterns, the varieties of symptoms and the different types of clinical evolution generate many diagnostic difficulties. Currently, MDD assessment is performed through the use of assessment scales and interviews with the patient. This highlights the need to adopt methods that can make an objective, rapid and effective diagnosis. Mental disorders and embodiment can alter the brain processes that are related to cognition and therefore to the motor system. As a result, movement analysis by means of wearable sensors is attracting the attention of clinicians as it represents a solution that can be quickly translated into ecological environments. In this study we explored the potential of an instrumented movement assessment, targeting the long-term goal of self-administered assessment in ecological settings. Using 4 inertial measurement units (IMUs), we tested parameters that could be predictive of pathology during a timed up and go test. By means of age/sex adjusted logistic regression we identified instrumental parameters significantly discriminating MDD patients and controls. Building on earlier findings in literature for pathology recognition in movement, a particular attention was devoted to parameters concerning movement complexity evaluated by multiscale entropy analysis of signals. Our tests confirmed that complexity parameters can significantly discriminate between patients and controls (Nagelkerke’s R 2 = 0.523). Figures Figure 1 Figure 2 1. Introduction Major Depressive Disorder (MDD) is a mental disorder representing one of the most important diseases affecting human health and longevity [ 1 ]. MDD can lead to disability and suicides and there have been over 800,000 cases of suicides globally [ 2 ] probably increasing alongside COVID-19 occurrence [ 3 ]. The World Health Organization has estimated that by 2030 an explosion of depression-disabling effects in society will affect health and quality of life. Moreover, MDD presents a heterogeneity of clinical features with up to 250 unique symptoms being recognized. Additionally, each of these clinical determinants can also derive from a disease different from MDD [ 4 ]. This, combined with the likely concurrent presence of MDD with other comorbidities (e.g., mental disorders or physical impairments), calls for a precise and accurate diagnosis. The MDD diagnosis is currently symptom-based and the evaluation of its severity mainly relies on psychological interviews and rating scales. These approaches show weak points since diagnosis is mainly made on subjective experiences and self-reports, often affected by memory biases [ 5 ]. The knowledge about MDD biological bases is still confined. Research, based on biomarkers, has not revealed sufficient specificity and sensitivity to be considered in clinical routine [ 6 ]. Despite the promising results of newly introduced techniques such as Magnetic Resonance Imaging (MRI) markers or electroencephalography (EEG) analysis of front-cortical patterns [ 7 ], their use in clinical practice requires cumbersome and expensive equipment as well as skilled technical personnel. MDD is a disorder that can cause a variety of emotional and physical problems leading to severe clinical implications [ 8 ], more common in young and postgraduate students [ 9 ]. For this reason, a quick and reliable method to detect the risk of MDD is needed. In this optic, within the last two decades, the relationship between the motor system and emotional processes has been in the spotlight. Attention has been paid to research on the embodiment effects of MDD which highlights the dual aspect concerning the bodily expression of emotion and the way in which emotions are elaborated [ 10 ]. It has been shown how motor behaviour is regulated by emotions, and how it represents an indicator of mental illness [ 11 ]. Therefore, mental disorders and embodiment could alter brain processes that are related to cognition and therefore the motor system [ 12 ], [ 13 ]. In particular, in preliminary work, it was demonstrated how MDD patients show a strong association between psychomotor deterioration and reduced walking speed, vertical head movements and a more slumped posture [ 10 ]. Consequently, instrumental movement and gait analysis (GA) represents a solution for objective assessment of MDD symptoms on movements. Moreover, it can be translatable to ecological settings and home environments using wearables [ 14 ]. In general, GA is an objective method often adopted in the field of neurology, psychology [ 12 ] and orthopaedics [ 15 ] covering a crucial role in the evaluation of movement in mood disorders too [ 16 ]. Several studies assessed posture and gait using different procedures and instruments [ 17 ], such as a computerized walkway, force platform and standardized protocols (e.g., Time Up and Go (TUG) test). The early studies on MDD spatiotemporal gait patterns compared healthy and pathological gait, investigating how mood influenced movement change [ 18 ], [ 19 ]. High-speed cameras and light barriers were adopted by Lemke et al. to investigate both spatial and temporal gait parameters in MDD and control participants [ 20 ]. Controls and depressed differed in gait velocity, which is slower in depressed participants, and it is associated with a shorter stride length. These results were confirmed by Michalak et al., that using stereophotogrammetry, found that participants with MDD present reduced arm swing and vertical head movements causing greater lateral swaying movements of the upper body [ 10 ]. Gait speed tests present a wide variety of methodologies from study to study, thus making a general description of MDD patients gait patterns very difficult to obtain [ 21 ]. In Biggs et al., the association between the risk of falls and depression in older adults has been investigated and a worse TUG performance was found in depression subject [ 22 ]. Current research on gait assessment tools is focusing on Inertial Measurement Unit (IMU) sensors [ 23 ]. To our knowledge, the use of such technology has not yet been adopted for the investigation of MDD severity. This kind of sensor has been widely exploited for other pathologies such as Parkinson’s disease, cognitive decline [ 24 ] and clinical application [ 25 ]. IMUs represent an available, inexpensive, and easy-to-use technology which can be embedded in daily-use smart devices and produce immediately translatable results. For these reasons, in this study, we investigated the signals from IMU-equipped wearable sensors in extracting objective gait parameters, during the TUG test, in both MDD patients and age-matched controls. We focused on gait complexity, viewed in terms of multiscale entropy (MSE). Our hypothesis is that movement complexity can potentially be used as a predictor of MDD occurrence. MSE has been used for analysing brain consciousness, electromyographic signals and EEG signals [ 26 ]. The Complexity Index (CI) which is a derived measure from MSE, is a recognized measure of the complexity of physiologic and physical signals of finite length [ 27 ]. MSE and CI found applications in evaluating bio-signals and to investigate the difference between healthy and pathological conditions. Lun Hsieh et al. showed how MSE and CI, applied to ground reaction force signals, can differentiate healthy participants from Parkinson’s disease patients [ 28 ]. CI has also found a place in the assessement of multiple sclerosis (SM), as demonstrated by Etzemuller et al., who investigated the CI of posturographic signals to compare SM patients with controls [ 29 ]. We therefore consider CI as a candidate to be analyzed in retrieving information on MDD assessment too. 2. Materials and Methods 2.1 Patients and Controls For this study, 63 students from the University of Padua (Padua, Italy) were enrolled. As pertains the group with depressive symptoms, the inclusion criteria were: i) age between 18 and 65 years; ii) depressive symptoms assessed with a score ≥ 12 at the Beck Depression Inventory 2nd edition (BDI-II; [ 30 ], [ 31 ]); iii) ability to express consent to participate in the study; iv) non-substance use disorder and no co-occurring psychiatric disorders (e.g., bipolar disorder, psychotic symptoms and suicide risk); v) no severe somatic conditions or medical conditions that may influence GA; vi) no necessity to assume drugs that can affect cardiac activity; vii) no psychopharmacological drug treatment (e.g., antidepressant, anxiolytic etc); viii) no pregnancy or breastfeeding. For the control group, the same inclusion criteria were applied with the exception that participants must report a BDI-II score < 12. All participants gave informed consent following Helsinki Declaration and, after signing it, they were subjected to an ad-hoc interview to collect sociodemographic (i.e., age, gender, education), and medical data (i.e., medical condition or pharmacological therapy). The Italian adaptation of the BDI-II was adopted to assess the presence and severity of depressive symptoms. The research protocol was approved by the local ethics committee (Comitato Etico per la Ricerca Psicologica (AREA 17), no. protocol 3727). 2.2 Experimental Protocol Each participant was asked to perform 3 trials of the TUG test at a comfortable walking speed in a laboratory environment. This kind of test is commonly used to evaluate mobility and balance and if coupled with inertial sensors it allows extracting parameters such as the number of steps, cadence and acceleration range [ 32 ]. The task consisted in standing up from a chair, walking on a fixed length, turning, walking back, and sitting again (Fig. 1 ). An extended version of the test was performed (i.e., the walking distance was extended to 7m instead of 3m) to maximise the likelihood of detecting the influence of depression on gait. To record gait data, participants were equipped with a network of 4 IMUs (Shimmer Sensing, Dublin, D17 H262, Ireland) placed at the right and left ankles just above the malleolus, at the pelvis on the back in correspondence of the fifth lumbar vertebra and at the trunk. Each inertial sensor frequency was set at 250 Hz and the X-Y-Z axes were aligned with the anatomical axes Antero-Posterior (AP), Cranio-Caudal (CC) and Medio-Lateral (ML) as summarized in Fig. 1 . The time needed to execute the test was monitored with a chronometer by experienced personnel. 2.3 Data Processing The pre-processing was computed using MATLAB® (Mathworks Inc., Natick MA) software. The high-frequency noise from IMU raw data was filtered with a lowpass filter (cut off frequency 4Hz). Data collected from IMU sensors were used to segment the TUG test into 3 main phases: i) the standing phase, ii) the walking phase (including walking forward, turning around the obstacle and walking back), iii) the sitting phase. The beginning of each trial was flagged from the experimenter through a button from the Shimmer Sensing UI. All phases were identified using the z-axis accelerometer data of the IMU positioned on the pelvis and the y-axis gyroscope of the IMUs on the ankles. Specifically, the first and last deflections of the pelvis accelerometer data were used to detect the standing and sitting activities, while the ankle angle velocities allowed to identify the beginning and the end of the walking phase. 2.4 Gait Parameters Selection and Estimation Before extracting parameters, gait events (Heel-Strike, HS; Mid Swing, MS; Toe-Off, TO) were detected from each ankle IMU signal. The HS, corresponding to the initial contact, was identified by searching for the zero-crossing event immediately after the CC acceleration peak [ 33 ]. The MS, which is the period in which the reference leg is in the air and the thigh is at its maximum flexion for the advancement of the limb, was found as the positive ML angular velocity peak [ 34 ]. The TO was detected by searching for the negative amplitude peak of the ML angular velocity before MS [ 35 ]. The time to perform the overall TUG test was measured. Gait parameters such as walking time, stride time, swing time, walking speed, number of steps and cadence (defined as steps per minute) were computed from extracted gait events and then were averaged across the 3 TUG repetitions of each participant. For each TUG phase, the acceleration range, covered by the pelvis and the trunk, was calculated for all three components (ML, AP, and CC). For what concerned gait quality, Multi-Scale Entropy (MSE) analysis is widely used to evaluate postural stability during the TUG test [ 36 ],[ 37 ] and it measures the complexity of times series at different time scales. The CI, corresponding to the area under MSE curves[ 38 ], [ 27 ] was calculated as follows: $$\sum _{\tau =1}^{n}SE\left(\tau \right)$$ with SE the Sample Entropy for each i-th scale where τ the i-th scale factor. Since the data points at the largest scale must correspond to at least 200 points, as suggested by Busa et al. [ 37 ], n = 5 was chosen. Entropy for each scale is obtained as indicated in Costa et al., [ 39 ] choosing the number of consecutive data points m = 2 and the tolerance for accepting the match r = 0.15. Parameters for the ankle were averaged for each direction between right and left obtained values. 2.5 Statistical analysis Demographic data were expressed as median and IQR as well as gait parameters and CI, given their non-Gaussian distribution (Shapiro-Wilk test). To evaluate the extracted parameters in terms of their capability in discriminating MDD patients and controls, a logistic regression, adjusted for age and sex, was conducted for each parameter, utilizing IBM SPSS Statistics 28. The dependent variable was binary and represented the group (controls, C, 0, depressed patients, D, 1). The contribution effect of variables to depression was measured by calculating the odds ratio (OR) and its confidence interval. In all analyses, a significance threshold for p-value = 0.05 was used. Receiver Operator Characteristic (ROC) curves and the Area under the Curve (AuC) for the significant variables were also computed. Finally, a multivariate assessment by logistic regression including all parameters that resulted significantly associated to MDD detection was conducted. Multicollinearity was tested by variance inflating factor (VIF) discarding predictors with VIF > 3. Linearity of the continuous variables with respect to the logit of the dependent variable was assessed via the Box-Tidwell (1962) procedure with Bonferroni correction [ 40 ]. Based on this assessment, all continuous independent variables were found to be linearly related to the logit of the dependent variable. 3. Results Sixty-three participants (30 controls, C, and 33 depressed patients, D) were enrolled. The C group included 8 males and 22 females (median age 23 years [IQR = 3], Table 1 ) and the D group included 5 males and 28 females (median age 21 years [IQR = 3]). The Mann-Whitney test showed a significant statistical difference (p < 0.05) in years of schooling between groups and BDI-II (Table 1 ). The BDI-II self-assessment report showed a median score of 6 [IQR = 5] for the C group, whereas for group D it had a median of 18 [IQR = 7]. The overall median duration of the TUG test was found to be 15.64 s [IQR = 3.66] for C and 15.24 s [IQR = 2.81] for D with no significant differences between the two groups (Mann-Whitney p = 0.962). Table 1 characteristics by study group Control (N = 30) Depressed (N = 33) P value Sex 8M 22F 5M 28F 0.259 Age 23 [ 3 ] 21 [ 3 ] 0.069 BMI 21.30 [3.87] 21.72 [5.53] 0.685 Schooling level 16.50 [ 3 ] 15 [ 2 ] < 0.05* BDI-II 6 [ 5 ] 18 [ 7 ] < 0.001* Table 2 shows the results of the descriptive analysis of gait instrumental parameters for the overall test and for the three phases in which the test was segmented. The results of the logistic regressions targeting group discrimination for each parameter (age/sex adjusted) were reported. Variables that showed significant group discrimination capability (p-value < 0.05) were highlighted. Odds ratio (with the 95% confidence interval) were also reported. No significant effects were observed for conventional parameters of TUG analysis such as the TUG time, the walking time, the number of steps or the cadence. Table 2 – Group comparison and univariate logistic regressions (age/sex adjusted). Control MDD Logistic regression 95% Conf. Interval Median [IQR] Median [IQR] B S.E. Wald p-value OR Inf Sup Overall TUG TUG Time, [s] 15.64 [3.66] 15.24 [2.81] 0.068 0.101 0.449 0.503 1.070 0.877 1.306 CI - Pelvis acc ML 3.08 [0.27] 3.12 [0.38] 0.064 0.870 0.005 0.941 1.066 0.194 5.869 CI - Pelvis acc AP 1.74 [0.25] 1.83 [0.63] 1.594 0.857 3.459 0.063 4.923 0.918 26.409 CI - Pelvis acc CC 2.59 [0.33] 2.70 [0.43] 1.804 0.919 3.853 0.05* 6.075 1.003 36.808 CI - Pelvis gyro ML 2.13 [0.73] 2.39 [0.96] 0.910 0.503 3.267 0.071 2.484 0.926 6.664 CI - Pelvis gyro AP 2.93 [0.59] 3.01 [1.02] 0.375 0.511 0.539 0.463 1.455 0.535 3.962 CI - Pelvis gyro CC 1.52 [053] 1.56 [0.49] 0.841 0.706 1.420 0.233 2.319 0.581 9.252 CI - Trunk acc ML 2.80 [0.49] 2.80 [0.47] 0.350 0.805 0.189 0.664 1.419 0.293 6.872 CI - Trunk acc AP 1.38 [0.49] 1.48 [0.40] 1.160 0.848 1.870 0.171 3.191 0.605 16.832 CI - Trunk acc CC 2.56 [0.35] 2.59 [0.34] -0.942 0.965 0.952 0.329 0.390 0.059 2.586 CI - Trunk gyro ML 2.08 [0.84] 2.17 [0.60] 0.377 0.523 0.519 0.471 1.457 0.523 4.061 CI - Trunk gyro AP 2.77 [0.84] 3.22 [0.89] 1.305 0.534 5.983 0.014* 3.689 1.296 10.50 CI - Trunk gyro CC 1.30 [0.37] 1.33 [0.47] 0.687 0.770 0.796 0.372 1.988 0.440 8.987 Walking phase Walking time, [s] 12.31 [3.76] 12.59 [2.83] 0.106 0.108 0.969 0.325 1.112 0.900 1.375 N step 11.75 [2.10] 11.05 [1.30] -0.118 0.233 0.255 0.613 0.889 0.562 1.404 Cadence, [N.strides/minute] 72.05 [12.49] 71.04 [8.05] 0.027 0.037 0.545 0.460 1.028 0.956 1.105 CI - Pelvis acc AP 3.33 [0.33] 3.33 [0.34] -0.347 0.875 0.157 0.692 0.707 0.127 3.931 CI - Pelvis acc ML 2.02 [0.36] 2.07 [0.74] 0.843 0.658 1.641 0.200 2.324 0.640 8.441 CI - Pelvis acc CC 2.69 [0.40] 2.84 [0.50] 1.500 0.859 3.05 0.081 4.483 0.832 24.144 CI - Pelvis gyro AP 3.05 [0.76] 3.24 [0.81] 0.411 0.414 0.987 0.320 1.509 0.670 3.396 CI - Pelvis gyro ML 3.22 [0.68] 3.04 [0.96] 0.010 0.481 0 0.984 1.010 0.393 2.594 CI - Pelvis gyro CC 1.70 [0.69] 1.79 [0.50] 0.254 0.414 0.376 0.540 1.289 0.573 2.900 CI - Trunk acc AP 2.04 [0.28] 2.17 [0.56] 0.419 0.785 0.285 0.593 1.521 0.327 7.086 CI - Trunk acc ML 2.96 [0.65] 2.93 [0.56] 1.084 0.739 2.152 0.142 2.957 0.695 12.594 CI - Trunk acc CC 2.74 [0.37] 2.68 [0.33] -1.215 1.052 1.334 0.248 0.297 0.038 2.332 CI - Trunk gyro AP 3.27 [0.84] 3.49 [1.04] 1.120 0.517 4.697 0.03* 3.064 1.113 8.437 CI - Trunk gyro ML 3.04 [0.75] 2.95 [1.01] 0.019 0.38 0.002 0.960 1.019 0.484 2.147 CI - Trunk gyro CC 1.59 [0.51] 1.51 [0.60] 0.167 0.500 0.111 0.739 1.181 0.444 3.145 CI - Right Ankle acc AP 1.42 [0.51] 1.49 [0.36] -0.491 0.879 0.312 0.577 0.612 0.109 3.430 CI - Right Ankle acc ML 2.15 [0.51] 2.06 [0.40] -1.143 0.868 2.431 0.188 0.319 0.058 1.749 CI - Right Ankle acc CC 0.87 [0.23] 0.85 [0.28] -0.826 1.111 0.553 0.457 0.438 0.050 3.861 CI - Right Ankle gyro AP 1.52 [0.38] 1.50 [0.36] -0.156 0.868 0.032 0.857 1.169 0.213 6.407 CI - Right Ankle gyro ML 1.04 [0.26] 1.05 [0.22] -0.422 1.454 0.084 0.772 0.656 0.038 11.329 CI - Right Ankle gyro CC 1.69 [0.42] 1.56 [0.44] -2.055 0.997 4.246 0.039* 0.128 0.018 0.905 CI - Left Ankle acc AP 1.49 [0.42] 1.46 [0.42] -0.877 0.935 0.879 0.348 0.416 0.067 2.602 CI - Left Ankle acc ML 2.23 [0.68] 2.05 [0.62] -1.567 0.818 3.675 0.055 0.209 0.042 1.036 CI - Left Ankle acc CC 0.87 [0.32] 0.84 [0.21] -1.237 1.171 1.115 0.291 0.290 0.029 2.884 CI - Left Ankle gyro AP 1.60 [0.40] 1.52 [0.44] -0.847 0.948 0.798 0.372 0.439 0.067 2.748 CI - Left Ankle gyro ML 1.02 [0.31] 1.05 [1.90] 0.111 1.508 0.005 0.942 1.117 0.058 21.482 CI - Left Ankle gyro CC 1.65 [0.43] 1.46 [0.36] -1.756 0.921 3.632 0.057 0.173 0.028 1.051 Standing phase CI - Pelvis acc ML 1.89 [0.80] 2.20 [0.60] 0.647 0.495 1.707 0.191 1.909 0.724 5.036 CI - Pelvis acc AP 0.89 [0.51] 0.93 [0.46] 0.338 0.759 0.198 0.656 1.402 0.317 6.210 CI - Pelvis acc CC 1.44 [0.41] 1.59 [0.61] 0.803 0.587 1.875 0.171 2.233 0.707 7.051 CI - Pelvis gyro ML 1.11 [0.54] 1.28 [0.67] 0.952 0.548 3.023 0.082 2.592 0.886 7.582 CI - Pelvis gyro AP 2.70 [0.68] 3.04 [0.94] 0.192 0.346 0.308 0.579 1.212 0.615 2.388 CI - Pelvis gyro CC 1.53 [0.67] 1.87 [0.70] 0.610 0.512 1.418 0.234 1.840 0.675 5.018 CI - Trunk acc ML 1.94 [0.69] 2.13 [0.84] 0.672 0.468 2.058 0.151 1.958 0.782 4.901 CI - Trunk acc AP 1.01 [0.26] 1.02 [0.27] 0.360 1.151 0.098 0.755 1.433 0.150 13.683 CI - Trunk acc CC 1.52 [0.45] 2.62 [0.41] 0.459 0.723 0.403 0.526 1.583 0.383 6.533 CI - Trunk gyro ML 1.13 [0.26] 1.18 [0.56] 0.918 0.681 1.820 0.177 2.505 0.660 9.509 CI - Trunk gyro AP 2.41 [1.07] 2.90 [1.00] 0.278 0.330 0.706 0.401 1.320 0.691 2.522 CI - Trunk gyro CC 1.95 [0.55] 1.65 [0.50] -1.231 0.619 3.948 0.047* 0.292 0.087 0.983 CI - Right Ankle acc ML 1.89 [0.71] 1.93 [0.69] -0.110 0.499 0.049 0.826 0.896 0.337 2.382 CI - Right Ankle acc AP 1.23 [0.68] 1.44 [0.78] 0.395 0.491 0.647 0.421 1.484 0.567 3.883 CI - Right Ankle acc CC 1.29 [0.90] 0.96 [0.68] -0.548 0.45 1.484 0.223 0.578 0.24 1.396 CI - Right Ankle gyro ML 1.04 [0.94] 1.12 [0.67] -0.031 0.501 0.004 0.951 0.97 0.363 2.589 CI - Right Ankle gyro AP 1.67 [0.31] 1.90 [0.81] 0.868 0.627 1.916 0.166 2.383 0.697 8.146 CI - Right Ankle gyro CC 1.77 [0.47] 1.73 [0.65] 0.453 0.555 0.667 0.414 1.573 0.530 4.665 CI - Left Ankle acc ML 2.23 [0.95] 2.11 [0.59] -0.045 0.476 0.009 0.924 0.956 0.376 2.43 CI - Left Ankle acc AP 1.27 [1.11] 1.48 [0.76] 0.213 0.481 0.197 0.657 1.238 0.482 3.178 CI - Left Ankle acc CC 0.83 [0.84] 1.10 [1.05] 0.968 0.499 3.759 0.053 2.632 0.990 6.999 CI - Left Ankle gyro ML 1.08 [0.76] 1.14 [0.51] 0.105 0.517 0.041 0.839 1.111 0.403 3.058 CI - Left Ankle gyro AP 1.61 [0.70] 1.61 [0.67] 0.191 0.672 0.081 0.776 1.210 0.324 4.516 CI - Left Ankle gyro CC 1.60 [0.50] 1.85 [0.64] 1.038 0.638 2.653 0.103 2.825 0.810 9.856 Sitting phase CI - Pelvis acc ML 2.00 [0.65] 2.22 [0.74] 1.181 0.553 4.562 0.033* 3.258 1.102 9.632 CI - Pelvis acc AP 1.08 [0.32] 1.13 [0.41] 1.626 0.972 2.801 0.094 5.085 0.757 34.159 CI - Pelvis acc CC 1.68 [0.48] 1.77 [0.69] 1.326 0.729 3.312 0.069 3.767 0.903 1.571 CI - Pelvis gyro ML 1.13 [0.62] 1.19 [0.61] 0.748 0.592 1.6 0.206 2.113 0.663 6.736 CI - Pelvis gyro AP 1.80 [1.09] 2.21 [1.11] 0.789 0.376 4.397 0.036* 2.201 1.053 4.601 CI - Pelvis gyro CC 0.49 [0.59] 0.61 [0.85] 0.990 0.628 2.486 0.115 2.692 0.786 9.216 CI - Trunk acc ML 2.13 [0.54] 2.43 [0.71] 1.575 0.658 5.721 0.017* 4.831 1.329 17.56 CI - Trunk acc AP 1.10 [0.32] 1.17 [0.42] 0.952 0.873 1.19 0.275 2.592 0.468 14.344 CI - Trunk acc CC 1.80 [0.57] 1.90 [0.48] 0.378 0.682 0.307 0.580 1.459 0.383 5.551 CI - Trunk gyro ML 1.19 [0.40] 1.28 [0.41] 0.274 0.632 0.188 0.665 1.315 0.381 4.543 CI - Trunk gyro AP 1.88 [1.23] 2.34 [1.18] 0.788 0.388 4.117 0.042* 2.199 1.027 4.708 CI - Trunk gyro CC 0.65 [0.51] 0.88 [0.68] 1.686 0.779 4.679 0.031* 5.396 1.172 24.851 CI - Right Ankle acc ML 1.06 [0.40] 1.30 [0.71] 1.394 0.683 4.162 0.041* 4.03 1.056 15.379 CI - Right Ankle acc AP 1.15 [0.54] 1.22 [0.71] 0.575 0.586 0.962 0.327 1.777 0.564 5.602 CI - Right Ankle acc CC 1.27 [0.48] 1.40 [0.71] 1.139 0.557 4.178 0.041* 3.124 1.048 9.314 CI - Right Ankle gyro ML 1.32 [0.57] 1.55 [0.58] 0.991 0.598 2.752 0.097 2.695 0.835 8.694 CI - Right Ankle gyro AP 1.16 [0.35] 1.32 [0.61] 0.867 0.623 1.934 0.164 2.379 0.701 8.067 CI - Right Ankle gyro CC 0.51 [0.48] 0.50 [0.63] 0.918 0.67 1.881 0.170 2.505 0.674 9.305 CI - Left Ankle acc ML 1.18 [0.36[ 1.33 [0.54] 1.494 0.809 3.409 0.065 4.455 0.912 21.759 CI - Left Ankle acc AP 1.57 [0–67] 1.50 [0.68] -0.048 0.567 0.007 0.933 0.953 0.314 2.897 CI - Left Ankle acc CC 1.52 [0.56] 1.50 [0.57] 0.492 0.51 0.933 0.334 1636 0.602 4445 CI - Left Ankle gyro ML 1.56 [0.94] 1.67 [0.91] 0.603 0.448 1.815 0.178 1.828 0.760 4.400 CI - Left Ankle gyro AP 1.33 [0.62] 1.40 [0.53] 0.342 0.619 0.305 0.581 1.408 0.418 4.736 CI - Left Ankle gyro CC 0.62 [0.53] 0.74 [0.55] 1.62 0.81 4.002 0.045* 5.055 1.033 24.725 Note: gyro = angular velocity; acc = acceleration; bold fonts and * indicate p < 0.05 ---TABLE 2 SHOULD BE PLACED AROUND HERE--- The ROC curve achievable by discriminating C and D groups by logistic regression was computed for variables showing statistically significant differences (Fig. 2 ). The standard error and the AuC are presented (Table 3 ). The best discrimination capability was obtained for the CI of Trunk AP angular velocity (AuC = 0.70, 95% confidence interval [0.51–0.79]) for the overall TUG test evaluation. The multicollinearity analysis on significant variables identified one variable with VIF > 3 that was discarded ( CI - Trunk AP angular velocity, walking ). The multivariate analysis (Table 4 ) confirmed the significance of two CI calculated on gyroscope data in the CC direction from the ankle during walking and from the trunk during the standing phase of the test. The multivariate analysis resulted in a model with Nagelkerke’s R 2 of 0.523. Table 3 – AuC and its 95% confidence interval of overall and segmented TUG parameters. Variables AUC Standard error Asymptotic 95% confidence interval Lower limit Upper limit CI - Pelvis CC acc (overall) 0.65 0.070 0.512 0.787 CI - Trunk AP gyro (overall) 0.70 0.066 0.574 0.834 CI - Trunk AP gyro (walking) 0.67 0.068 0.539 0.804 CI - Ankle CC gyro (walking) 0.65 0.070 0.510 0.783 CI - Trunk CC gyro (standing) 0,69 0.070 0.518 0.791 CI - Pelvis ML acc (sitting) 0.65 0.067 0.552 0.814 CI - Pelvis AP gyro (sitting) 0.65 0.069 0.520 0.790 CI - Trunk ML acc (sitting) 0.68 0.070 0.498 0.773 CI - Trunk AP gyro (sitting) 0.65 0.069 0.516 0.789 CI - Trunk CC gyro (sitting) 0.64 0.071 0.481 0.758 CI - Right Ankle ML acc (sitting) 0.65 0.070 0.501 0.777 CI - Right Ankle CC acc (sitting) 0.62 0.068 0.561 0.839 CI - Left Ankle CC gyro (sitting) 0.64 0.069 0.534 0.803 Note: gyro = angular velocity; acc = acceleration Table 4 – Multivariate logistic regression results (* indicate p < 0.05) 95% Conf. Interval B S.E. Wald p-value OR Inf Sup Sex (m) -0.199 1.036 0.037 0.847 0.819 0.107 6.244 Age -0.128 0.175 0.539 0.463 0.880 0.624 1.239 CI - Pelvis acc VT (overall) 2.122 1.492 2.024 0.155 8.351 0.449 155.394 CI - Trunk AP gyro (overall) 1.444 1.235 1.367 0.242 4.238 0.376 47.713 CI - Right Ankle CC gyro (walking) -3.590 1.673 4.605 0.032* 0.028 0.001 0.733 CI - Trunk CC gyro (standing) -1.808 0.916 3.896 0.048* 0.164 0.027 0.987 CI - Pelvis ML acc (sitting) 0.575 0.971 0.351 0.554 1.777 0.265 11.906 CI - Pelvis AP gyro (sitting) 0.319 0.607 0.276 0.599 1.376 0.419 4.521 CI - Trunk ML acc (sitting) 0.334 1.041 0.103 0.749 1.396 0.181 10.746 CI - Trunk AP gyro (sitting) -0.166 0.813 0.042 0.838 0.847 0.172 4.167 CI - Trunk CC gyro (sitting) 1.001 1.242 0.649 0.421 2.720 0.238 31.045 CI - Right Ankle ML acc (sitting) -0.368 1.257 0.086 0.770 0.692 0.059 8.127 CI - Right Ankle CC acc (sitting) 1.586 0.981 2.613 0.106 4.885 0.714 33.422 CI - Left Ankle CC gyro (sitting) -0.309 1.467 0.044 0.833 0.734 0.041 13.024 Constant term -2.614 5.513 0.225 0.635 0.073 Note: gyro = angular velocity; acc = acceleration; * indicate p < 0.05 Nagelkerke’s R 2 = 0.523 4. Discussion In this study, we hypothesised that biomechanical determinants, extracted from IMU sensors, can be useful for the diagnosis of depression since the psychomotor ability is very much reflected in movement [ 41 ]. Our study focused in particular on CI as a candidate marker of depression. We considered data acquired from wearables during a simple test, targeting the long-term objective of a self-administered test to be done in home settings. Anti-depressant medication such as selective serotonin-reuptake inhibitor (SSRI) was found associated with the development of disorders of movement [ 42 ]. However, none of the participants in our sample was under anti-depressant medication for at least 1 month prior the assessment. Most available IMUs applications for pathological movement assessment are dedicated to the analysis of neuromotor impairments such as in Parkinson’s disease [ 43 ] or in post-stroke patients [ 44 ]. Early applications in psychological assessment using wearable movement sensors are limited to the estimation of sedentary levels [ 45 ]. However, despite the lack of an ecological movement assessment of depression patients using wearables, the assessment of movement using other technical approaches is not new. For example, walking speed resulted lower in depressed patients than in controls in a review conducted by Murri et al. on the instrumental analysis of the gait [ 17 ]. The speed decrease has been justified as a search for stability during the execution of motion tasks. Our results do not confirm this earlier finding: walking time on the fixed length path of 7 meters is indeed not discriminating the two groups (Table 2 ). However, it is worth noting that 16 out of 21 studies from the review by Murri et al. [ 17 ] were carried out on participants older than 65 years. Our study included 22 years old participants on average with mild to moderate depression. The reason of our results could then be identified on the short length of the walking path (7m) and on the age of participants. Indeed, our result is coherent with the study of Kumar et al. which showed no relationship between gait speed and MDD in young adults [ 46 ]. Patience et al. in their study address the fact that there is a link between executive functioning, depressive symptoms and the slow gait specific for depression in old age [ 47 ]. CIs identifies some significant discrimination capabilities. In general terms, low values of CIs indicate low information content signals, suggesting a degeneration of systems underlying postural/movement control [ 37 ]. One of the CIs that turns out to be a predictor of depression concerns the trunk angular velocity measured in AP direction in both the overall TUG test and the walking phase. This parameter was significantly higher in depressed participants (Table 2 ). More complex oscillations and lower stability of the trunk has already been observed in this population. Michalak et al. found that lateral body swaying is significantly higher in the depressed cohort [ 10 ]. Nevertheless, Adolph et al. justify such correlation with the fact that patients are forced to follow a straight line, imposing an external constraint on the walk that could have larger effect on this cohort. By calculating the complexity coefficients from the IMU signals placed on the ankles, the CI of the angular velocity of the right ankle along the CC direction is lower for MDD. This decrease in the complexity of the movement released by the ankle during the walking phase could be accentuated especially by the turning around the obstacles, which in our case, has not been analysed separately from the walking phase. On the contrary, during the sitting phase, the ankle movement complexity is higher in MDD patients. We may speculate that this higher complexity of the movement in patients during postural transitions, could be associated with a continuous search for stability or a less oriented movement. To our knowledge, no study compared CI between depressed patients and age-matched controls. Earlier studies involving this category of parameters mainly focused on predicting the risk of falling in the elderly [ 28 ],[ 38 ],[ 48 ]. Nevertheless, the obtained parameters that are proved to be predictive of the MDD, indicate that both the standing phase (trunk AP acceleration range and CI of the CC trunk angular velocity) and the sitting phase (CI of pelvis ML acceleration, CI of pelvis AP angular velocity, CI of trunk ML acceleration, CI of trunk AP and CC angular velocity, CI of right ankle ML and CC acceleration and CI of the left ankle CC angular velocity) store meaningful information on the posture stability which may be altered by the presence of depression. The multivariate analysis confirmed the usability of some of the CI as depression markers. The two variables that confirmed the significant effect are both referring to angular velocities in the vertical direction, so mainly related during walking to the turning phase of the TUG and during the standing phase to the oscillations during the postural transition. These results calls for additional tests to be conducted in future to clarify the relevance of such variables in dedicated tests, directly targeting the turning movement or the postural transition in different conditions. The main limitations of this study concern the number of samples. Increasing this number would allow considering a wider age range and include other cohorts, as the one used is circumspect to students. Further biomechanical parameters, which can be useful for the detection of MDD given their correlation with gait quality and stability, should be analysed such as Lyapunov coefficients, stride regularity and harmonic ratios. In particular, the latter would allow for a finer estimate of respectively stability, gait consistency and symmetry. Nevertheless, such parameters could not be computed in our study given the short length of the recorded time series. 5. Conclusions We proposed the assessment of depressed patients and controls by means of instrumented TUG test. Results suggest that indicators of depression may be found in instrumentally derived movement assessment. The identification of depression markers from wearable sensors could enable the long-term objective of depression mass screening using portable devices and machine learning methods. A such long-term goal, which will require the definition of more complex diagnostic models and their validation in larger cohorts, is still encouraged by our preliminary findings. Declarations Ethics approval and consent to participate The study was conducted following the Helsinki Declaration and it was approved by the local ethical committee (Comitato Etico per la Ricerca Psicologica, AREA 17, no. protocol 3727). The informed consent for the use of data was collected afterwards. Competing interests The authors declare that they have no competing interests. Funding The study was supported by the Italian Ministry of University and Research under the PRIN2017 program, TRAINED project (g.a. 2017L2RLZ2) and by the Italian Ministry of Health under the Ricerca Corrente program and the Ricerca Finalizzata program (COAIDER project g.a. RF-2021-12375472). Author Contribution Conceptualisation, A.M.; Methodology: E.V., L.T., G.F., A.M..; Formal analysis: G.F. and A.M.; Investigation: E.P., F.M. and L.T. ; Resources: C.G.,C.C., A.M.; Data Curation: L.T., G.F., E.P., F.M.; Writing—original draft preparation: G.F; Visualization: G.F.; Supervision: C.G.,C.C., A.M.; Funding Acquisition: A.M. C.G.All authors reviewed the manuscript and approved the submitted version. Acknowledgement The study was supported by the Italian Ministry of University and Research under the PRIN2017 program, TRAINED project (g.a. 2017L2RLZ2) and by the Italian Ministry of Health under the Ricerca Corrente program and the Ricerca Finalizzata program (COAIDER project g.a. RF-2021-12375472). Data Availability Data can be shared for replication and research purposes on request to the authors. References Dai L, Zhou H, Xu X, Zuo eZ. «Brain structural and functional changes in patients with major depressive disorder: A literature review», PeerJ , vol. 2019, fasc. 11, pp. 1–17, 2019, 10.7717/peerj.8170 . Otte C. «Major depressive disorder», Nat. Rev. Diseade Primer , fasc. 16065 (2016), 2016. Kaimin KL, Li Y, Wu. «Be Prepared for a Mental Illness Pandemic in China: Too Early to Celebrate the Victory Over COVID-19». Disaster Med Public Health Prep. 2021. doi.org/10.1017/dmp.2021.11 . Yun-Ai-Su TS. «Progress and challenges in research of the mechanisms of anhedonia in major depressive disorder», Gen Psychiatr , vol. 35(1): e10, 2022, 10.1136/gpsych-2021-100724 . Collier S, Monette P, Hobbs K, Tabasky E, Forester BP, Vahia eIV. «Mapping Movement: Applying Motion Measurement Technologies to the Psychiatric Care of Older Adults», Curr. Psychiatry Rep. , vol. 20, fasc. 8, pp. 4–11, 2018, 10.1007/s11920-018-0921-z . Nobis A, Zalewski D, Waszkiewicz eN. «Peripheral markers of depression», J. Clin. Med. , vol. 9, fasc. 12, pp. 1–54, 2020, 10.3390/jcm9123793 . Gentili C. «Why do we keep failing in identifying reliable biological markers in depression?», J. Evid.-Based Psychother. , vol. 17, fasc. 2, pp. 69–84, 2017, 10.24193/jebp.2017.2.4 . Lewinsohn PM, Rohde P, Seeley eJR. «Major depressive disorder in older adolescents: Prevalence, risk factors, and clinical implications», Clin. Psychol. Rev. , vol. 18, fasc. 7, pp. 765–794, 1998, 10.1016/S0272-7358(98)00010-5 . Cooper KM, Gin LE, Barnes ME, Brownell eSE. «An exploratory study of students with depression in undergraduate research experiences», CBE Life Sci. Educ. , vol. 19, fasc. 2, 2020, 10.1187/cbe.19-11-0217 . Michalak J, Troje NF, Fischer J, Vollmar P, Heidenreich T, Schulte eD. «Embodiment of sadness and depression-gait patterns associated with dysphoric mood», Psychosom. Med. , vol. 71, fasc. 5, pp. 580–587, 2009, 10.1097/PSY.0b013e3181a2515c . Engstrom EJ, Weber eMM. «The directions of psychiatric research by Emil Kraepelin», Hist. Psychiatry , vol. 16, fasc. 3, pp. 345–364, 2005, 10.1177/0957154X05056763 . Sanders RD e, Gillig PM. «Gait and its assessment in psychiatry», Psychiatry Edgemont , vol. 7, fasc. 7, pp. 38–43, 2010. Feldman R, -Schreiber S, Been eE. «Gait, Balance and Posture in Major Mental Illnesses: Depression, Anxiety and Schizophrenia Reconstruction of hominin posture View project Ein Qashish View project», Austin Med Sci , vol. 5, fasc. 1, p. 1039, 2020. Shao W. «A Multi-modal Gait Analysis-based Depression Detection System». IEEE J Biomed Health Inf Early Access, 2021. Brunnekreef JJ, Van Uden CJT, Van Moorsel S, Kooloos eJGM. «Reliability of videotaped observational gait analysis in patients with orthopedic impairments». BMC Musculoskelet Disord. 2005;6:1–9. 10.1186/1471-2474-6-17 . Deligianni F, Guo Y, Yang eGZ. «From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology», IEEE J. Biomed. Health Inform. , vol. 23, fasc. 6, pp. 2302–2316, 2019, 10.1109/JBHI.2019.2938111 . Belvederi Murri M et al. November,., «Instrumental assessment of balance and gait in depression: A systematic review», Psychiatry Res. , vol. 284, fasc. 2020, 10.1016/j.psychres.2019.112687 . Montepare JM, Goldstein SB, Clausen eA. «The identification of emotions from gait information», J. Nonverbal Behav. , vol. 11, fasc. 1, pp. 33–42, 1987, 10.1007/BF00999605 . Godfrey e HP, Knight RG. «The validity of actometer and speech activity measures in the assessment of depressed patients». Br J Psychiatry. 1984;145:159–63. 10.1192/bjp.145.2.159 . fasc. AUG. Lemke MR, Wendorff T, Mieth B, Buhl K, Linnemann eM. «Spatiotemporal gait patterns during over ground locomotion in major depression compared with healthy controls», J. Psychiatr. Res. , vol. 34, fasc. 4–5, pp. 277–283, 2000, 10.1016/S0022-3956(00)00017-0 . Graham JE, Ostir GV, Fisher SR, Ottenbacher eKJ. «Assessing walking speed in clinical research: a systematic review», J. Eval. Clin. Pract. , vol. 14, fasc. 4, pp. 552–562, 2008, 10.1111/j.1365-2753.2007.00917.x . Briggs R, Kennelly SP, Kenny eRA. «Does baseline depression increase the risk of unexplained and accidental falls in a cohort of community-dwelling older people? Data from The Irish Longitudinal Study on Ageing (TILDA)», Int. J. Geriatr. Psychiatry , vol. 33, fasc. 2, pp. e205–e211, 2018, 10.1002/gps.4770 . Cho YS et al. «Evaluation of validity and reliability of inertial measurement unit-based gait analysis systems», Ann. Rehabil. Med. , vol. 42, fasc. 6, pp. 872–883, 2018, 10.5535/arm.2018.42.6.872 . Klucken J et al. «Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson’s Disease», PLoS ONE , vol. 8, fasc. 2, 2013, 10.1371/journal.pone.0056956 . Pope J, Lee TKM, Tamjidi L, Mirzaei eA. «A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications», vol. 11, pp. 177–194, 2018. Lu WY, Chen JY, Chang CF, Weng WC, Lee WT, Shieh eJS. «Multiscale entropy of electroencephalogram as a potential predictor for the prognosis of neonatal seizures», PLoS ONE , vol. 10, fasc. 12, pp. 1–11, 2015, 10.1371/journal.pone.0144732 . Costa M, Goldberger AL, Peng eCK. «Multiscale entropy analysis of biological signals», Phys. Rev. E - Stat. Nonlinear Soft Matter Phys. , vol. 71, fasc. 2, pp. 1–18, 2005, 10.1103/PhysRevE.71.021906 . Hsieh YL, Abbod eMF. «Gait analyses of parkinson’s disease patients using multiscale entropy», Electron. Switz. , vol. 10, fasc. 21, 2021, 10.3390/electronics10212604 . Etzelmueller MS, Yap SM, O’Keeffe C, Gaughan M, McGuigan C, Reilly eRB. «Multiscale entropy derived complexity index analysis demonstrates significant mediolateral sway in persons with multiple sclerosis compared to healthy controls», Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS , vol. 2020-July, pp. 5176–5179, 2020, 10.1109/EMBC44109.2020.9175672 . Beck AT, Steer RA, Brown eGK. Beck Depression Inventory. Second Edition Manual. San Antonio, TX: The Psychological Corporation Harcourt Brace & Company; 1996. Sica C, Coradeschi D, Ghisi M, Sanavio eE. Beck Anxiety Inventory. Adattamento Italiano: Manuale. Florence: Organizzazioni Speciali; 2006. Zakaria NA, Kuwae Y, Tamura T, Minato K, Kanaya eS. «Quantitative analysis of fall risk using TUG test», Comput. Methods Biomech. Biomed. Engin. , vol. 18, fasc. 4, pp. 426–437, 2015, 10.1080/10255842.2013.805211 . Jasiewicz JM et al. «Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals», Gait Posture , vol. 24, fasc. 4, pp. 502–509, 2006, 10.1016/j.gaitpost.2005.12.017 . Gujarathi e T, Bhole K. «Gait Analysis Using Imu Sensor», 2019 10th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2019 , pp. 6–10, 2019, 10.1109/ICCCNT45670.2019.8944545 . Arshad MZ, Jung D, Park M, Mun KR, Kim eJ. «Gait-based Human Identification through Minimum Gait-phases and Sensors», Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS , fasc. Ml, pp. 7044–7049, 2021, 10.1109/EMBC46164.2021.9630468 . Riva M et al. «Glioma biopsies classification using raman spectroscopy and machine learning models on fresh tissue samples», Cancers , vol. 13, fasc. 5, pp. 1–14, 2021, 10.3390/cancers13051073 . Busa e MA, van Emmerik REA. «Multiscale entropy: A tool for understanding the complexity of postural control», J. Sport Health Sci. , vol. 5, fasc. 1, pp. 44–51, 2016, 10.1016/j.jshs.2016.01.018 . Wu CH, Lee CH, Jiang BC, Sun eTL. «Multiscale entropy analysis of postural stability for estimating fall risk via domain knowledge of timed-up-and-go accelerometer data for elderly people living in a community», Entropy , vol. 21, fasc. 11, 2019, 10.3390/e21111076 . Costa M, Peng CK, Goldberger AL, Hausdorff eJM. «Multiscale entropy analysis of human gait dynamics», Phys. Stat. Mech. Its Appl. , vol. 330, fasc. 1–2, pp. 53–60, 2003, 10.1016/j.physa.2003.08.022 . Tabachnick BG, Fidell eLS. Using multivariate statistics , Sixth. Pearson New Int Ed, 2019. Consultato: 24 agosto 2023. [Online]. Disponibile su: https://www.pearsonhighered.com/assets/preface/0/1/3/4/0134790545.pdf . Sobin e C, Sackeim HA. «Psychomotor symptoms of depression.», Am. J. Psychiatry , vol. 154, fasc. 1, pp. 4–17, gen. 1997. Gerber PE, Lynd eLD. «Selective Serotonin-Reuptake Inhibitor–Induced Movement Disorders», Ann. Pharmacother. , vol. 32, fasc. 6, pp. 692–698, giu. 1998, 10.1345/aph.17302 . Pinto C et al. «Movement smoothness during a functional mobility task in subjects with Parkinson’s disease and freezing of gait - An analysis using inertial measurement units», J. NeuroEngineering Rehabil. , vol. 16, fasc. 1, pp. 1–14, 2019, 10.1186/s12984-019-0579-8 . Peters DM, et al. «Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review». J Neuroeng Rehabil. apr. 2021;18:67. 10.1186/s12984-021-00863-x . fasc. 1. Vancampfort D et al. «Sedentary behavior and physical activity levels in people with schizophrenia, bipolar disorder and major depressive disorder: a global systematic review and meta-analysis», World Psychiatry Off. J. World Psychiatr. Assoc. WPA , vol. 16, fasc. 3, pp. 308–315, ott. 2017, 10.1002/wps.20458 . Kumar D, Villarreal DJ, Meuret eAE. «Walking on the bright side: Associations between affect, depression, and gait», PLoS ONE , vol. 16, fasc. 12 December, pp. 1–19, 2021, 10.1371/journal.pone.0260893 . Patience J, Lai KSP, Russell E, Vasudev A, Montero-Odasso M, Burhan eAM. «Relationship Between Mood, Thinking, and Walking: A Systematic Review Examining Depressive Symptoms, Executive Function, and Gait», Am. J. Geriatr. Psychiatry , vol. 27, fasc. 12, pp. 1375–1383, 2019, 10.1016/j.jagp.2019.07.007 . Lee CH, Sun TL, Jiang BC, Choi eVH. «Using wearable accelerometers in a community service context to categorize falling behavior», Entropy , vol. 18, fasc. 7, pp. 1–14, 2016, 10.3390/e18070257 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4174631","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286610580,"identity":"327edb10-9ebc-4989-84c9-98ece997beab","order_by":0,"name":"Giulia Fransvea","email":"","orcid":"","institution":"IRCCS Fondazione Don Carlo Gnocchi","correspondingAuthor":false,"prefix":"","firstName":"Giulia","middleName":"","lastName":"Fransvea","suffix":""},{"id":286610582,"identity":"cb070a24-c206-4e58-89a3-9ad1853e4a31","order_by":1,"name":"Elisabetta Patron","email":"","orcid":"","institution":"Università di Padova","correspondingAuthor":false,"prefix":"","firstName":"Elisabetta","middleName":"","lastName":"Patron","suffix":""},{"id":286610584,"identity":"0e1cb830-3c13-4d19-9041-1171c10ee262","order_by":2,"name":"Eleonora Vendrame","email":"","orcid":"","institution":"Scuola Superiore Sant'Anna","correspondingAuthor":false,"prefix":"","firstName":"Eleonora","middleName":"","lastName":"Vendrame","suffix":""},{"id":286610587,"identity":"b3783489-808a-4f14-bd97-feede692c40a","order_by":3,"name":"Luigi Truppa","email":"","orcid":"","institution":"IRCCS Fondazione Don Carlo Gnocchi","correspondingAuthor":false,"prefix":"","firstName":"Luigi","middleName":"","lastName":"Truppa","suffix":""},{"id":286610588,"identity":"302f115e-94a0-4e85-8a2d-31244071a7cf","order_by":4,"name":"Francesca Mura","email":"","orcid":"","institution":"Università di Padova","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Mura","suffix":""},{"id":286610590,"identity":"5d2a2cc1-4c77-4059-b07f-a21dd87d1b70","order_by":5,"name":"Christian Cipriani","email":"","orcid":"","institution":"Scuola Superiore Sant'Anna","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Cipriani","suffix":""},{"id":286610592,"identity":"abd58f35-af47-474d-a4c5-c03f3e9bbb7e","order_by":6,"name":"Claudio Gentili","email":"","orcid":"","institution":"Università di Padova","correspondingAuthor":false,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Gentili","suffix":""},{"id":286610594,"identity":"3019f4c0-77a3-4e97-823e-43d540a4226e","order_by":7,"name":"Andrea Mannini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYNACAwYefoYEKEeCsQHOxqtFsgFFC2E9QF0H4FpABB4t/NMOP3tcUHBHxvh48rGHX3fYyPPPbm5gePgDtxaJ22nmxjMMnvGYnXmWbix7Js1wxp2DBBx2O8FMmsfgMI/ZjRwzacm2wwkGEon4tcjfTv8G1mI8g1gtBrdzILYYSOSYSX4kRovh7ZwysBaJM8/SpBnbgH65kdhwICENtxa52+nbpHn+HLbnb08+JvmzDRhiM9IfPvxhg8f7yICZB8o4QKQGBgZGPLExCkbBKBgFIxgAACjbT60kY5hrAAAAAElFTkSuQmCC","orcid":"","institution":"IRCCS Fondazione Don Carlo Gnocchi","correspondingAuthor":true,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Mannini","suffix":""}],"badges":[],"createdAt":"2024-03-27 08:42:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4174631/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4174631/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54159036,"identity":"0491f784-f613-4894-9999-86732a0402ae","added_by":"auto","created_at":"2024-04-05 12:47:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":332257,"visible":true,"origin":"","legend":"\u003cp\u003eTUG test phases: the black rectangles represent the imu.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4174631/v1/e251386dbffb427cecaa6798.png"},{"id":54159037,"identity":"7c9176dc-34ca-4eed-a590-41880f41eb99","added_by":"auto","created_at":"2024-04-05 12:47:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129227,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curves for the overall tug (A) and for the 3 individual segmented phases of the tug test: walking (B), standing (C), sitting (D).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4174631/v1/d0fec3aa94cd3a015a6ae3e1.png"},{"id":60532966,"identity":"b2911c78-53bd-4ab0-90b2-09cefdd85d39","added_by":"auto","created_at":"2024-07-17 21:31:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2066962,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4174631/v1/f693167e-4bd2-4fe7-8934-609965f8dd7b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Complexity indexes from instrumented timed-up-and-go test reveal depression-related movement differences","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMajor Depressive Disorder (MDD) is a mental disorder representing one of the most important diseases affecting human health and longevity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. MDD can lead to disability and suicides and there have been over 800,000 cases of suicides globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] probably increasing alongside COVID-19 occurrence [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The World Health Organization has estimated that by 2030 an explosion of depression-disabling effects in society will affect health and quality of life. Moreover, MDD presents a heterogeneity of clinical features with up to 250 unique symptoms being recognized. Additionally, each of these clinical determinants can also derive from a disease different from MDD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This, combined with the likely concurrent presence of MDD with other comorbidities (e.g., mental disorders or physical impairments), calls for a precise and accurate diagnosis.\u003c/p\u003e \u003cp\u003eThe MDD diagnosis is currently symptom-based and the evaluation of its severity mainly relies on psychological interviews and rating scales. These approaches show weak points since diagnosis is mainly made on subjective experiences and self-reports, often affected by memory biases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe knowledge about MDD biological bases is still confined. Research, based on biomarkers, has not revealed sufficient specificity and sensitivity to be considered in clinical routine [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite the promising results of newly introduced techniques such as Magnetic Resonance Imaging (MRI) markers or electroencephalography (EEG) analysis of front-cortical patterns [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], their use in clinical practice requires cumbersome and expensive equipment as well as skilled technical personnel.\u003c/p\u003e \u003cp\u003eMDD is a disorder that can cause a variety of emotional and physical problems leading to severe clinical implications [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], more common in young and postgraduate students [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For this reason, a quick and reliable method to detect the risk of MDD is needed. In this optic, within the last two decades, the relationship between the motor system and emotional processes has been in the spotlight. Attention has been paid to research on the embodiment effects of MDD which highlights the dual aspect concerning the bodily expression of emotion and the way in which emotions are elaborated [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It has been shown how motor behaviour is regulated by emotions, and how it represents an indicator of mental illness [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, mental disorders and embodiment could alter brain processes that are related to cognition and therefore the motor system [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In particular, in preliminary work, it was demonstrated how MDD patients show a strong association between psychomotor deterioration and reduced walking speed, vertical head movements and a more slumped posture [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsequently, instrumental movement and gait analysis (GA) represents a solution for objective assessment of MDD symptoms on movements. Moreover, it can be translatable to ecological settings and home environments using wearables [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In general, GA is an objective method often adopted in the field of neurology, psychology [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and orthopaedics [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] covering a crucial role in the evaluation of movement in mood disorders too [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Several studies assessed posture and gait using different procedures and instruments [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], such as a computerized walkway, force platform and standardized protocols (e.g., Time Up and Go (TUG) test). The early studies on MDD spatiotemporal gait patterns compared healthy and pathological gait, investigating how mood influenced movement change [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. High-speed cameras and light barriers were adopted by Lemke et al. to investigate both spatial and temporal gait parameters in MDD and control participants [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Controls and depressed differed in gait velocity, which is slower in depressed participants, and it is associated with a shorter stride length. These results were confirmed by Michalak et al., that using stereophotogrammetry, found that participants with MDD present reduced arm swing and vertical head movements causing greater lateral swaying movements of the upper body [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Gait speed tests present a wide variety of methodologies from study to study, thus making a general description of MDD patients gait patterns very difficult to obtain [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In Biggs et al., the association between the risk of falls and depression in older adults has been investigated and a worse TUG performance was found in depression subject [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent research on gait assessment tools is focusing on Inertial Measurement Unit (IMU) sensors [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To our knowledge, the use of such technology has not yet been adopted for the investigation of MDD severity. This kind of sensor has been widely exploited for other pathologies such as Parkinson\u0026rsquo;s disease, cognitive decline [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and clinical application [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. IMUs represent an available, inexpensive, and easy-to-use technology which can be embedded in daily-use smart devices and produce immediately translatable results.\u003c/p\u003e \u003cp\u003eFor these reasons, in this study, we investigated the signals from IMU-equipped wearable sensors in extracting objective gait parameters, during the TUG test, in both MDD patients and age-matched controls. We focused on gait complexity, viewed in terms of multiscale entropy (MSE). Our hypothesis is that movement complexity can potentially be used as a predictor of MDD occurrence. MSE has been used for analysing brain consciousness, electromyographic signals and EEG signals [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The Complexity Index (CI) which is a derived measure from MSE, is a recognized measure of the \u003cem\u003ecomplexity\u003c/em\u003e of physiologic and physical signals of finite length [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. MSE and CI found applications in evaluating bio-signals and to investigate the difference between healthy and pathological conditions. Lun Hsieh et al. showed how MSE and CI, applied to ground reaction force signals, can differentiate healthy participants from Parkinson\u0026rsquo;s disease patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. CI has also found a place in the assessement of multiple sclerosis (SM), as demonstrated by Etzemuller et al., who investigated the CI of posturographic signals to compare SM patients with controls [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We therefore consider CI as a candidate to be analyzed in retrieving information on MDD assessment too.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients and Controls\u003c/h2\u003e \u003cp\u003eFor this study, 63 students from the University of Padua (Padua, Italy) were enrolled. As pertains the group with depressive symptoms, the inclusion criteria were: i) age between 18 and 65 years; ii) depressive symptoms assessed with a score\u0026thinsp;\u0026ge;\u0026thinsp;12 at the Beck Depression Inventory 2nd edition (BDI-II; [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]); iii) ability to express consent to participate in the study; iv) non-substance use disorder and no co-occurring psychiatric disorders (e.g., bipolar disorder, psychotic symptoms and suicide risk); v) no severe somatic conditions or medical conditions that may influence GA; vi) no necessity to assume drugs that can affect cardiac activity; vii) no psychopharmacological drug treatment (e.g., antidepressant, anxiolytic etc); viii) no pregnancy or breastfeeding.\u003c/p\u003e \u003cp\u003eFor the control group, the same inclusion criteria were applied with the exception that participants must report a BDI-II score\u0026thinsp;\u0026lt;\u0026thinsp;12.\u003c/p\u003e \u003cp\u003e All participants gave informed consent following Helsinki Declaration and, after signing it, they were subjected to an ad-hoc interview to collect sociodemographic (i.e., age, gender, education), and medical data (i.e., medical condition or pharmacological therapy). The Italian adaptation of the BDI-II was adopted to assess the presence and severity of depressive symptoms.\u003c/p\u003e \u003cp\u003eThe research protocol was approved by the local ethics committee (Comitato Etico per la Ricerca Psicologica (AREA 17), no. protocol 3727).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cem\u003eExperimental Protocol\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eEach participant was asked to perform 3 trials of the TUG test at a comfortable walking speed in a laboratory environment. This kind of test is commonly used to evaluate mobility and balance and if coupled with inertial sensors it allows extracting parameters such as the number of steps, cadence and acceleration range [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The task consisted in standing up from a chair, walking on a fixed length, turning, walking back, and sitting again (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). An extended version of the test was performed (i.e., the walking distance was extended to 7m instead of 3m) to maximise the likelihood of detecting the influence of depression on gait. To record gait data, participants were equipped with a network of 4 IMUs (Shimmer Sensing, Dublin, D17 H262, Ireland) placed at the right and left ankles just above the malleolus, at the pelvis on the back in correspondence of the fifth lumbar vertebra and at the trunk. Each inertial sensor frequency was set at 250 Hz and the X-Y-Z axes were aligned with the anatomical axes Antero-Posterior (AP), Cranio-Caudal (CC) and Medio-Lateral (ML) as summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The time needed to execute the test was monitored with a chronometer by experienced personnel.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 \u003cem\u003eData Processing\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe pre-processing was computed using MATLAB\u0026reg; (Mathworks Inc., Natick MA) software. The high-frequency noise from IMU raw data was filtered with a lowpass filter (cut off frequency 4Hz). Data collected from IMU sensors were used to segment the TUG test into 3 main phases: i) the standing phase, ii) the walking phase (including walking forward, turning around the obstacle and walking back), iii) the sitting phase. The beginning of each trial was flagged from the experimenter through a button from the Shimmer Sensing UI. All phases were identified using the z-axis accelerometer data of the IMU positioned on the pelvis and the y-axis gyroscope of the IMUs on the ankles. Specifically, the first and last deflections of the pelvis accelerometer data were used to detect the standing and sitting activities, while the ankle angle velocities allowed to identify the beginning and the end of the walking phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 \u003cem\u003eGait Parameters Selection and Estimation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eBefore extracting parameters, gait events (Heel-Strike, HS; Mid Swing, MS; Toe-Off, TO) were detected from each ankle IMU signal. The HS, corresponding to the initial contact, was identified by searching for the zero-crossing event immediately after the CC acceleration peak [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The MS, which is the period in which the reference leg is in the air and the thigh is at its maximum flexion for the advancement of the limb, was found as the positive ML angular velocity peak [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The TO was detected by searching for the negative amplitude peak of the ML angular velocity before MS [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe time to perform the overall TUG test was measured. Gait parameters such as walking time, stride time, swing time, walking speed, number of steps and cadence (defined as steps per minute) were computed from extracted gait events and then were averaged across the 3 TUG repetitions of each participant. For each TUG phase, the acceleration range, covered by the pelvis and the trunk, was calculated for all three components (ML, AP, and CC).\u003c/p\u003e \u003cp\u003eFor what concerned gait quality, Multi-Scale Entropy (MSE) analysis is widely used to evaluate postural stability during the TUG test [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e],[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and it measures the complexity of times series at different time scales. The CI, corresponding to the area under MSE curves[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\sum _{\\tau =1}^{n}SE\\left(\\tau \\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewith SE the Sample Entropy for each i-th scale where τ the i-th scale factor. Since the data points at the largest scale must correspond to at least 200 points, as suggested by Busa et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5 was chosen. Entropy for each scale is obtained as indicated in Costa et al., [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] choosing the number of consecutive data points m\u0026thinsp;=\u0026thinsp;2 and the tolerance for accepting the match r\u0026thinsp;=\u0026thinsp;0.15. Parameters for the ankle were averaged for each direction between right and left obtained values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eDemographic data were expressed as median and IQR as well as gait parameters and CI, given their non-Gaussian distribution (Shapiro-Wilk test). To evaluate the extracted parameters in terms of their capability in discriminating MDD patients and controls, a logistic regression, adjusted for age and sex, was conducted for each parameter, utilizing IBM SPSS Statistics 28. The dependent variable was binary and represented the group (controls, C, 0, depressed patients, D, 1). The contribution effect of variables to depression was measured by calculating the odds ratio (OR) and its confidence interval. In all analyses, a significance threshold for p-value\u0026thinsp;=\u0026thinsp;0.05 was used.\u003c/p\u003e \u003cp\u003eReceiver Operator Characteristic (ROC) curves and the Area under the Curve (AuC) for the significant variables were also computed. Finally, a multivariate assessment by logistic regression including all parameters that resulted significantly associated to MDD detection was conducted. Multicollinearity was tested by variance inflating factor (VIF) discarding predictors with VIF\u0026thinsp;\u0026gt;\u0026thinsp;3. Linearity of the continuous variables with respect to the logit of the dependent variable was assessed via the Box-Tidwell (1962) procedure with Bonferroni correction [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Based on this assessment, all continuous independent variables were found to be linearly related to the logit of the dependent variable.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eSixty-three participants (30 controls, C, and 33 depressed patients, D) were enrolled. The C group included 8 males and 22 females (median age 23 years [IQR\u0026thinsp;=\u0026thinsp;3], Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the D group included 5 males and 28 females (median age 21 years [IQR\u0026thinsp;=\u0026thinsp;3]). The Mann-Whitney test showed a significant statistical difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in years of schooling between groups and BDI-II (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The BDI-II self-assessment report showed a median score of 6 [IQR\u0026thinsp;=\u0026thinsp;5] for the C group, whereas for group D it had a median of 18 [IQR\u0026thinsp;=\u0026thinsp;7].\u003c/p\u003e \u003cp\u003eThe overall median duration of the TUG test was found to be 15.64 s [IQR\u0026thinsp;=\u0026thinsp;3.66] for C and 15.24 s [IQR\u0026thinsp;=\u0026thinsp;2.81] for D with no significant differences between the two groups (Mann-Whitney p\u0026thinsp;=\u0026thinsp;0.962).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003echaracteristics by study group\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;30)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDepressed\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;33)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8M 22F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5M 28F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.30 [3.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.72 [5.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchooling level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.50 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the descriptive analysis of gait instrumental parameters for the overall test and for the three phases in which the test was segmented. The results of the logistic regressions targeting group discrimination for each parameter (age/sex adjusted) were reported. Variables that showed significant group discrimination capability (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were highlighted. Odds ratio (with the 95% confidence interval) were also reported. No significant effects were observed for conventional parameters of TUG analysis such as the TUG time, the walking time, the number of steps or the cadence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e\u0026ndash; Group comparison and univariate logistic regressions (age/sex adjusted).\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eLogistic regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e95% Conf. Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall TUG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG Time, [s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.64 [3.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.24 [2.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.08 [0.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.12 [0.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74 [0.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83 [0.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e26.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Pelvis acc CC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.59 [0.33]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.70 [0.43]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.804\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.919\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.853\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.05*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e6.075\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e36.808\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.13 [0.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39 [0.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.93 [0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.01 [1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 [053]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56 [0.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e9.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.80 [0.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.80 [0.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38 [0.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48 [0.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e16.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.56 [0.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.59 [0.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.08 [0.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17 [0.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e4.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Trunk gyro AP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.77 [0.84]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.22 [0.89]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.305\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.534\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5.983\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.014*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.689\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.296\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e10.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk gyro CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 [0.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 [0.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e8.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWalking phase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalking time, [s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.31 [3.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.59 [2.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN step\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.75 [2.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.05 [1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence, [N.strides/minute]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.05 [12.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.04 [8.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33 [0.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33 [0.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.02 [0.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.07 [0.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e8.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.69 [0.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.84 [0.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e24.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.05 [0.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.24 [0.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.22 [0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.04 [0.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70 [0.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79 [0.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.04 [0.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17 [0.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e7.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.96 [0.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.93 [0.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e12.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.74 [0.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68 [0.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Trunk gyro AP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.27 [0.84]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.49 [1.04]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.120\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.517\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.697\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.03*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.064\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.113\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e8.437\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.04 [0.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.95 [1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk gyro CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 [0.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51 [0.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42 [0.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49 [0.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15 [0.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06 [0.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 [0.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 [0.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle gyro AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 [0.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 [0.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 [0.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 [0.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e11.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Right Ankle gyro CC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.69 [0.42]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.56 [0.44]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.055\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.997\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.246\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.039*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.128\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.905\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49 [0.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46 [0.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23 [0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05 [0.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 [0.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 [0.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle gyro AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 [0.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52 [0.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 [0.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 [1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e21.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle gyro CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65 [0.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46 [0.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStanding phase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89 [0.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20 [0.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 [0.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 [0.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44 [0.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59 [0.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e7.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 [0.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 [0.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e7.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.70 [0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.04 [0.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53 [0.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87 [0.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94 [0.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13 [0.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e4.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 [0.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 [0.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e13.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 [0.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.62 [0.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 [0.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 [0.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e9.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk gyro AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41 [1.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.90 [1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Trunk gyro CC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.95 [0.55]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.65 [0.50]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-1.231\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.619\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.948\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.047*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.292\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.087\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.983\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89 [0.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93 [0.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 [0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44 [0.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29 [0.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 [0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 [0.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 [0.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle gyro AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67 [0.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90 [0.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e8.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle gyro CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77 [0.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73 [0.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e4.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23 [0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11 [0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27 [1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48 [0.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83 [0.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 [1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 [0.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 [0.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle gyro AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61 [0.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61 [0.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e4.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle gyro CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 [0.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85 [0.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e9.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSitting phase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Pelvis acc ML\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.00 [0.65]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.22 [0.74]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.181\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.553\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.562\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.033*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.258\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.102\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e9.632\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 [0.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 [0.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e34.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68 [0.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77 [0.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 [0.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 [0.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Pelvis gyro AP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.80 [1.09]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.21 [1.11]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.789\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.376\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.397\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.036*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.201\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.053\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e4.601\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis gyro CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 [0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61 [0.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e9.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Trunk acc ML\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.13 [0.54]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.43 [0.71]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.575\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.658\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5.721\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.017*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4.831\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.329\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e17.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 [0.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 [0.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e14.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.80 [0.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90 [0.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 [0.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 [0.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e4.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Trunk gyro AP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.88 [1.23]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.34 [1.18]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.788\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.388\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.117\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.042*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.199\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e4.708\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Trunk gyro CC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.65 [0.51]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.88 [0.68]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.686\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.779\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.679\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.031*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e5.396\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.172\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e24.851\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Right Ankle acc ML\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.06 [0.40]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.30 [0.71]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.394\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.683\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.162\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.041*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.056\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e15.379\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 [0.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 [0.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Right Ankle acc CC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.27 [0.48]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.40 [0.71]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.139\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.557\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.178\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.041*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.124\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e9.314\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32 [0.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55 [0.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e8.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle gyro AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16 [0.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32 [0.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e8.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle gyro CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51 [0.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50 [0.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e9.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle acc ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 [0.36[\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 [0.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e21.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle acc AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57 [0\u0026ndash;67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 [0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle acc CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 [0.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 [0.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e4445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle gyro ML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56 [0.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67 [0.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e4.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle gyro AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33 [0.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 [0.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e4.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Left Ankle gyro CC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.62 [0.53]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.74 [0.55]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.045*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e5.055\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e24.725\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: gyro\u0026thinsp;=\u0026thinsp;angular velocity; acc\u0026thinsp;=\u0026thinsp;acceleration; bold fonts and * indicate p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e---TABLE 2 SHOULD BE PLACED AROUND HERE---\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe ROC curve achievable by discriminating C and D groups by logistic regression was computed for variables showing statistically significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The standard error and the AuC are presented (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The best discrimination capability was obtained for the CI of Trunk AP angular velocity (AuC\u0026thinsp;=\u0026thinsp;0.70, 95% confidence interval [0.51\u0026ndash;0.79]) for the overall TUG test evaluation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe multicollinearity analysis on significant variables identified one variable with VIF\u0026thinsp;\u0026gt;\u0026thinsp;3 that was discarded (\u003cem\u003eCI - Trunk AP angular velocity, walking\u003c/em\u003e). The multivariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) confirmed the significance of two CI calculated on gyroscope data in the CC direction from the ankle during walking and from the trunk during the standing phase of the test. The multivariate analysis resulted in a model with Nagelkerke\u0026rsquo;s R\u003csup\u003e2\u003c/sup\u003e of 0.523.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e\u0026ndash; AuC and its 95% confidence interval of overall and segmented TUG parameters.\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAsymptotic 95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis CC acc (overall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk AP gyro (overall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk AP gyro (walking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Ankle CC gyro (walking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk CC gyro (standing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis ML acc (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis AP gyro (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk ML acc (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk AP gyro (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk CC gyro (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle ML acc (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle CC acc (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle CC gyro (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: gyro\u0026thinsp;=\u0026thinsp;angular velocity; acc\u0026thinsp;=\u0026thinsp;acceleration\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e\u0026ndash; Multivariate logistic regression results (* indicate p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% Conf. Interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSup\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis acc VT (overall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e155.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk AP gyro (overall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Right Ankle CC gyro (walking)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-3.590\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.673\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.605\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.032*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.733\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI - Trunk CC gyro (standing)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-1.808\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.916\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.896\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.048*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.164\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.987\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis ML acc (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Pelvis AP gyro (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk ML acc (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk AP gyro (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Trunk CC gyro (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle ML acc (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Right Ankle CC acc (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCI - Left Ankle CC gyro (sitting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstant term\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e-2.614\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.513\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.225\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.635\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.073\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: gyro\u0026thinsp;=\u0026thinsp;angular velocity; acc\u0026thinsp;=\u0026thinsp;acceleration; * indicate p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNagelkerke\u0026rsquo;s R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.523\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we hypothesised that biomechanical determinants, extracted from IMU sensors, can be useful for the diagnosis of depression since the psychomotor ability is very much reflected in movement [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our study focused in particular on CI as a candidate marker of depression. We considered data acquired from wearables during a simple test, targeting the long-term objective of a self-administered test to be done in home settings. Anti-depressant medication such as selective serotonin-reuptake inhibitor (SSRI) was found associated with the development of disorders of movement [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, none of the participants in our sample was under anti-depressant medication for at least 1 month prior the assessment.\u003c/p\u003e \u003cp\u003eMost available IMUs applications for pathological movement assessment are dedicated to the analysis of neuromotor impairments such as in Parkinson\u0026rsquo;s disease [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] or in post-stroke patients [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Early applications in psychological assessment using wearable movement sensors are limited to the estimation of sedentary levels [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, despite the lack of an ecological movement assessment of depression patients using wearables, the assessment of movement using other technical approaches is not new. For example, walking speed resulted lower in depressed patients than in controls in a review conducted by Murri et al. on the instrumental analysis of the gait [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The speed decrease has been justified as a search for stability during the execution of motion tasks. Our results do not confirm this earlier finding: walking time on the fixed length path of 7 meters is indeed not discriminating the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, it is worth noting that 16 out of 21 studies from the review by Murri et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] were carried out on participants older than 65 years. Our study included 22 years old participants on average with mild to moderate depression. The reason of our results could then be identified on the short length of the walking path (7m) and on the age of participants. Indeed, our result is coherent with the study of Kumar et al. which showed no relationship between gait speed and MDD in young adults [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Patience et al. in their study address the fact that there is a link between executive functioning, depressive symptoms and the slow gait specific for depression in old age [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCIs identifies some significant discrimination capabilities. In general terms, low values of CIs indicate low information content signals, suggesting a degeneration of systems underlying postural/movement control [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. One of the CIs that turns out to be a predictor of depression concerns the trunk angular velocity measured in AP direction in both the overall TUG test and the walking phase. This parameter was significantly higher in depressed participants (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). More complex oscillations and lower stability of the trunk has already been observed in this population. Michalak et al. found that lateral body swaying is significantly higher in the depressed cohort [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nevertheless, Adolph et al. justify such correlation with the fact that patients are forced to follow a straight line, imposing an external constraint on the walk that could have larger effect on this cohort.\u003c/p\u003e \u003cp\u003eBy calculating the complexity coefficients from the IMU signals placed on the ankles, the CI of the angular velocity of the right ankle along the CC direction is lower for MDD. This decrease in the complexity of the movement released by the ankle during the walking phase could be accentuated especially by the turning around the obstacles, which in our case, has not been analysed separately from the walking phase. On the contrary, during the sitting phase, the ankle movement complexity is higher in MDD patients. We may speculate that this higher complexity of the movement in patients during postural transitions, could be associated with a continuous search for stability or a less oriented movement.\u003c/p\u003e \u003cp\u003eTo our knowledge, no study compared CI between depressed patients and age-matched controls. Earlier studies involving this category of parameters mainly focused on predicting the risk of falling in the elderly [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e],[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e],[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Nevertheless, the obtained parameters that are proved to be predictive of the MDD, indicate that both the standing phase (trunk AP acceleration range and CI of the CC trunk angular velocity) and the sitting phase (CI of pelvis ML acceleration, CI of pelvis AP angular velocity, CI of trunk ML acceleration, CI of trunk AP and CC angular velocity, CI of right ankle ML and CC acceleration and CI of the left ankle CC angular velocity) store meaningful information on the posture stability which may be altered by the presence of depression. The multivariate analysis confirmed the usability of some of the CI as depression markers. The two variables that confirmed the significant effect are both referring to angular velocities in the vertical direction, so mainly related during walking to the turning phase of the TUG and during the standing phase to the oscillations during the postural transition. These results calls for additional tests to be conducted in future to clarify the relevance of such variables in dedicated tests, directly targeting the turning movement or the postural transition in different conditions.\u003c/p\u003e \u003cp\u003eThe main limitations of this study concern the number of samples. Increasing this number would allow considering a wider age range and include other cohorts, as the one used is circumspect to students. Further biomechanical parameters, which can be useful for the detection of MDD given their correlation with gait quality and stability, should be analysed such as Lyapunov coefficients, stride regularity and harmonic ratios. In particular, the latter would allow for a finer estimate of respectively stability, gait consistency and symmetry. Nevertheless, such parameters could not be computed in our study given the short length of the recorded time series.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWe proposed the assessment of depressed patients and controls by means of instrumented TUG test. Results suggest that indicators of depression may be found in instrumentally derived movement assessment. The identification of depression markers from wearable sensors could enable the long-term objective of depression mass screening using portable devices and machine learning methods. A such long-term goal, which will require the definition of more complex diagnostic models and their validation in larger cohorts, is still encouraged by our preliminary findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e The study was conducted following the Helsinki Declaration and it was approved by the local ethical committee (Comitato Etico per la Ricerca Psicologica, AREA 17, no. protocol 3727). The informed consent for the use of data was collected afterwards.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was supported by the Italian Ministry of University and Research under the PRIN2017 program, TRAINED project (g.a. 2017L2RLZ2) and by the Italian Ministry of Health under the Ricerca Corrente program and the Ricerca Finalizzata program (COAIDER project g.a. RF-2021-12375472).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualisation, A.M.; Methodology: E.V., L.T., G.F., A.M..; Formal analysis: G.F. and A.M.; Investigation: E.P., F.M. and L.T. ; Resources: C.G.,C.C., A.M.; Data Curation: L.T., G.F., E.P., F.M.; Writing\u0026mdash;original draft preparation: G.F; Visualization: G.F.; Supervision: C.G.,C.C., A.M.; Funding Acquisition: A.M. C.G.All authors reviewed the manuscript and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe study was supported by the Italian Ministry of University and Research under the PRIN2017 program, TRAINED project (g.a. 2017L2RLZ2) and by the Italian Ministry of Health under the Ricerca Corrente program and the Ricerca Finalizzata program (COAIDER project g.a. RF-2021-12375472).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData can be shared for replication and research purposes on request to the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDai L, Zhou H, Xu X, Zuo eZ. \u0026laquo;Brain structural and functional changes in patients with major depressive disorder: A literature review\u0026raquo;, \u003cem\u003ePeerJ\u003c/em\u003e, vol. 2019, fasc. 11, pp. 1\u0026ndash;17, 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7717/peerj.8170\u003c/span\u003e\u003cspan address=\"10.7717/peerj.8170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtte C. \u0026laquo;Major depressive disorder\u0026raquo;, \u003cem\u003eNat. Rev. Diseade Primer\u003c/em\u003e, fasc. 16065 (2016), 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaimin KL, Li Y, Wu. \u0026laquo;Be Prepared for a Mental Illness Pandemic in China: Too Early to Celebrate the Victory Over COVID-19\u0026raquo;. Disaster Med Public Health Prep. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1017/dmp.2021.11\u003c/span\u003e\u003cspan address=\"10.1017/dmp.2021.11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYun-Ai-Su TS. \u0026laquo;Progress and challenges in research of the mechanisms of anhedonia in major depressive disorder\u0026raquo;, \u003cem\u003eGen Psychiatr\u003c/em\u003e, vol. 35(1): e10, 2022, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gpsych-2021-100724\u003c/span\u003e\u003cspan address=\"10.1136/gpsych-2021-100724\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollier S, Monette P, Hobbs K, Tabasky E, Forester BP, Vahia eIV. \u0026laquo;Mapping Movement: Applying Motion Measurement Technologies to the Psychiatric Care of Older Adults\u0026raquo;, \u003cem\u003eCurr. Psychiatry Rep.\u003c/em\u003e, vol. 20, fasc. 8, pp. 4\u0026ndash;11, 2018, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11920-018-0921-z\u003c/span\u003e\u003cspan address=\"10.1007/s11920-018-0921-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNobis A, Zalewski D, Waszkiewicz eN. \u0026laquo;Peripheral markers of depression\u0026raquo;, \u003cem\u003eJ. Clin. Med.\u003c/em\u003e, vol. 9, fasc. 12, pp. 1\u0026ndash;54, 2020, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm9123793\u003c/span\u003e\u003cspan address=\"10.3390/jcm9123793\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGentili C. \u0026laquo;Why do we keep failing in identifying reliable biological markers in depression?\u0026raquo;, \u003cem\u003eJ. Evid.-Based Psychother.\u003c/em\u003e, vol. 17, fasc. 2, pp. 69\u0026ndash;84, 2017, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.24193/jebp.2017.2.4\u003c/span\u003e\u003cspan address=\"10.24193/jebp.2017.2.4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewinsohn PM, Rohde P, Seeley eJR. \u0026laquo;Major depressive disorder in older adolescents: Prevalence, risk factors, and clinical implications\u0026raquo;, \u003cem\u003eClin. Psychol. Rev.\u003c/em\u003e, vol. 18, fasc. 7, pp. 765\u0026ndash;794, 1998, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0272-7358(98)00010-5\u003c/span\u003e\u003cspan address=\"10.1016/S0272-7358(98)00010-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooper KM, Gin LE, Barnes ME, Brownell eSE. \u0026laquo;An exploratory study of students with depression in undergraduate research experiences\u0026raquo;, \u003cem\u003eCBE Life Sci. Educ.\u003c/em\u003e, vol. 19, fasc. 2, 2020, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1187/cbe.19-11-0217\u003c/span\u003e\u003cspan address=\"10.1187/cbe.19-11-0217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichalak J, Troje NF, Fischer J, Vollmar P, Heidenreich T, Schulte eD. \u0026laquo;Embodiment of sadness and depression-gait patterns associated with dysphoric mood\u0026raquo;, \u003cem\u003ePsychosom. Med.\u003c/em\u003e, vol. 71, fasc. 5, pp. 580\u0026ndash;587, 2009, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/PSY.0b013e3181a2515c\u003c/span\u003e\u003cspan address=\"10.1097/PSY.0b013e3181a2515c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngstrom EJ, Weber eMM. \u0026laquo;The directions of psychiatric research by Emil Kraepelin\u0026raquo;, \u003cem\u003eHist. Psychiatry\u003c/em\u003e, vol. 16, fasc. 3, pp. 345\u0026ndash;364, 2005, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0957154X05056763\u003c/span\u003e\u003cspan address=\"10.1177/0957154X05056763\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanders RD e, Gillig PM. \u0026laquo;Gait and its assessment in psychiatry\u0026raquo;, \u003cem\u003ePsychiatry Edgemont\u003c/em\u003e, vol. 7, fasc. 7, pp. 38\u0026ndash;43, 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeldman R, -Schreiber S, Been eE. \u0026laquo;Gait, Balance and Posture in Major Mental Illnesses: Depression, Anxiety and Schizophrenia Reconstruction of hominin posture View project Ein Qashish View project\u0026raquo;, \u003cem\u003eAustin Med Sci\u003c/em\u003e, vol. 5, fasc. 1, p. 1039, 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao W. \u0026laquo;A Multi-modal Gait Analysis-based Depression Detection System\u0026raquo;. IEEE J Biomed Health Inf Early Access, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunnekreef JJ, Van Uden CJT, Van Moorsel S, Kooloos eJGM. \u0026laquo;Reliability of videotaped observational gait analysis in patients with orthopedic impairments\u0026raquo;. BMC Musculoskelet Disord. 2005;6:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2474-6-17\u003c/span\u003e\u003cspan address=\"10.1186/1471-2474-6-17\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeligianni F, Guo Y, Yang eGZ. \u0026laquo;From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology\u0026raquo;, \u003cem\u003eIEEE J. Biomed. Health Inform.\u003c/em\u003e, vol. 23, fasc. 6, pp. 2302\u0026ndash;2316, 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/JBHI.2019.2938111\u003c/span\u003e\u003cspan address=\"10.1109/JBHI.2019.2938111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelvederi Murri M et al. November,., \u0026laquo;Instrumental assessment of balance and gait in depression: A systematic review\u0026raquo;, \u003cem\u003ePsychiatry Res.\u003c/em\u003e, vol. 284, fasc. 2020, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psychres.2019.112687\u003c/span\u003e\u003cspan address=\"10.1016/j.psychres.2019.112687\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontepare JM, Goldstein SB, Clausen eA. \u0026laquo;The identification of emotions from gait information\u0026raquo;, \u003cem\u003eJ. Nonverbal Behav.\u003c/em\u003e, vol. 11, fasc. 1, pp. 33\u0026ndash;42, 1987, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/BF00999605\u003c/span\u003e\u003cspan address=\"10.1007/BF00999605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGodfrey e HP, Knight RG. \u0026laquo;The validity of actometer and speech activity measures in the assessment of depressed patients\u0026raquo;. Br J Psychiatry. 1984;145:159\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1192/bjp.145.2.159\u003c/span\u003e\u003cspan address=\"10.1192/bjp.145.2.159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. fasc. AUG.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemke MR, Wendorff T, Mieth B, Buhl K, Linnemann eM. \u0026laquo;Spatiotemporal gait patterns during over ground locomotion in major depression compared with healthy controls\u0026raquo;, \u003cem\u003eJ. Psychiatr. Res.\u003c/em\u003e, vol. 34, fasc. 4\u0026ndash;5, pp. 277\u0026ndash;283, 2000, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0022-3956(00)00017-0\u003c/span\u003e\u003cspan address=\"10.1016/S0022-3956(00)00017-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraham JE, Ostir GV, Fisher SR, Ottenbacher eKJ. \u0026laquo;Assessing walking speed in clinical research: a systematic review\u0026raquo;, \u003cem\u003eJ. Eval. Clin. Pract.\u003c/em\u003e, vol. 14, fasc. 4, pp. 552\u0026ndash;562, 2008, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-2753.2007.00917.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2753.2007.00917.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBriggs R, Kennelly SP, Kenny eRA. \u0026laquo;Does baseline depression increase the risk of unexplained and accidental falls in a cohort of community-dwelling older people? Data from The Irish Longitudinal Study on Ageing (TILDA)\u0026raquo;, \u003cem\u003eInt. J. Geriatr. Psychiatry\u003c/em\u003e, vol. 33, fasc. 2, pp. e205\u0026ndash;e211, 2018, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/gps.4770\u003c/span\u003e\u003cspan address=\"10.1002/gps.4770\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho YS et al. \u0026laquo;Evaluation of validity and reliability of inertial measurement unit-based gait analysis systems\u0026raquo;, \u003cem\u003eAnn. Rehabil. Med.\u003c/em\u003e, vol. 42, fasc. 6, pp. 872\u0026ndash;883, 2018, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5535/arm.2018.42.6.872\u003c/span\u003e\u003cspan address=\"10.5535/arm.2018.42.6.872\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlucken J et al. \u0026laquo;Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson\u0026rsquo;s Disease\u0026raquo;, \u003cem\u003ePLoS ONE\u003c/em\u003e, vol. 8, fasc. 2, 2013, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0056956\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0056956\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePope J, Lee TKM, Tamjidi L, Mirzaei eA. \u0026laquo;A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications\u0026raquo;, vol. 11, pp. 177\u0026ndash;194, 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu WY, Chen JY, Chang CF, Weng WC, Lee WT, Shieh eJS. \u0026laquo;Multiscale entropy of electroencephalogram as a potential predictor for the prognosis of neonatal seizures\u0026raquo;, \u003cem\u003ePLoS ONE\u003c/em\u003e, vol. 10, fasc. 12, pp. 1\u0026ndash;11, 2015, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0144732\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0144732\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta M, Goldberger AL, Peng eCK. \u0026laquo;Multiscale entropy analysis of biological signals\u0026raquo;, \u003cem\u003ePhys. Rev. E - Stat. Nonlinear Soft Matter Phys.\u003c/em\u003e, vol. 71, fasc. 2, pp. 1\u0026ndash;18, 2005, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1103/PhysRevE.71.021906\u003c/span\u003e\u003cspan address=\"10.1103/PhysRevE.71.021906\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsieh YL, Abbod eMF. \u0026laquo;Gait analyses of parkinson\u0026rsquo;s disease patients using multiscale entropy\u0026raquo;, \u003cem\u003eElectron. Switz.\u003c/em\u003e, vol. 10, fasc. 21, 2021, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/electronics10212604\u003c/span\u003e\u003cspan address=\"10.3390/electronics10212604\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEtzelmueller MS, Yap SM, O\u0026rsquo;Keeffe C, Gaughan M, McGuigan C, Reilly eRB. \u0026laquo;Multiscale entropy derived complexity index analysis demonstrates significant mediolateral sway in persons with multiple sclerosis compared to healthy controls\u0026raquo;, \u003cem\u003eProc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS\u003c/em\u003e, vol. 2020-July, pp. 5176\u0026ndash;5179, 2020, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/EMBC44109.2020.9175672\u003c/span\u003e\u003cspan address=\"10.1109/EMBC44109.2020.9175672\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeck AT, Steer RA, Brown eGK. Beck Depression Inventory. Second Edition Manual. San Antonio, TX: The Psychological Corporation Harcourt Brace \u0026amp; Company; 1996.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSica C, Coradeschi D, Ghisi M, Sanavio eE. Beck Anxiety Inventory. Adattamento Italiano: Manuale. Florence: Organizzazioni Speciali; 2006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZakaria NA, Kuwae Y, Tamura T, Minato K, Kanaya eS. \u0026laquo;Quantitative analysis of fall risk using TUG test\u0026raquo;, \u003cem\u003eComput. Methods Biomech. Biomed. Engin.\u003c/em\u003e, vol. 18, fasc. 4, pp. 426\u0026ndash;437, 2015, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10255842.2013.805211\u003c/span\u003e\u003cspan address=\"10.1080/10255842.2013.805211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJasiewicz JM et al. \u0026laquo;Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals\u0026raquo;, \u003cem\u003eGait Posture\u003c/em\u003e, vol. 24, fasc. 4, pp. 502\u0026ndash;509, 2006, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.gaitpost.2005.12.017\u003c/span\u003e\u003cspan address=\"10.1016/j.gaitpost.2005.12.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGujarathi e T, Bhole K. \u0026laquo;Gait Analysis Using Imu Sensor\u0026raquo;, 2019 \u003cem\u003e10th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2019\u003c/em\u003e, pp. 6\u0026ndash;10, 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ICCCNT45670.2019.8944545\u003c/span\u003e\u003cspan address=\"10.1109/ICCCNT45670.2019.8944545\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArshad MZ, Jung D, Park M, Mun KR, Kim eJ. \u0026laquo;Gait-based Human Identification through Minimum Gait-phases and Sensors\u0026raquo;, \u003cem\u003eProc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS\u003c/em\u003e, fasc. Ml, pp. 7044\u0026ndash;7049, 2021, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/EMBC46164.2021.9630468\u003c/span\u003e\u003cspan address=\"10.1109/EMBC46164.2021.9630468\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiva M et al. \u0026laquo;Glioma biopsies classification using raman spectroscopy and machine learning models on fresh tissue samples\u0026raquo;, \u003cem\u003eCancers\u003c/em\u003e, vol. 13, fasc. 5, pp. 1\u0026ndash;14, 2021, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers13051073\u003c/span\u003e\u003cspan address=\"10.3390/cancers13051073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusa e MA, van Emmerik REA. \u0026laquo;Multiscale entropy: A tool for understanding the complexity of postural control\u0026raquo;, \u003cem\u003eJ. Sport Health Sci.\u003c/em\u003e, vol. 5, fasc. 1, pp. 44\u0026ndash;51, 2016, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jshs.2016.01.018\u003c/span\u003e\u003cspan address=\"10.1016/j.jshs.2016.01.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu CH, Lee CH, Jiang BC, Sun eTL. \u0026laquo;Multiscale entropy analysis of postural stability for estimating fall risk via domain knowledge of timed-up-and-go accelerometer data for elderly people living in a community\u0026raquo;, \u003cem\u003eEntropy\u003c/em\u003e, vol. 21, fasc. 11, 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/e21111076\u003c/span\u003e\u003cspan address=\"10.3390/e21111076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta M, Peng CK, Goldberger AL, Hausdorff eJM. \u0026laquo;Multiscale entropy analysis of human gait dynamics\u0026raquo;, \u003cem\u003ePhys. Stat. Mech. Its Appl.\u003c/em\u003e, vol. 330, fasc. 1\u0026ndash;2, pp. 53\u0026ndash;60, 2003, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.physa.2003.08.022\u003c/span\u003e\u003cspan address=\"10.1016/j.physa.2003.08.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabachnick BG, Fidell eLS. \u003cem\u003eUsing multivariate statistics\u003c/em\u003e, Sixth. Pearson New Int Ed, 2019. Consultato: 24 agosto 2023. [Online]. Disponibile su: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pearsonhighered.com/assets/preface/0/1/3/4/0134790545.pdf\u003c/span\u003e\u003cspan address=\"https://www.pearsonhighered.com/assets/preface/0/1/3/4/0134790545.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSobin e C, Sackeim HA. \u0026laquo;Psychomotor symptoms of depression.\u0026raquo;, \u003cem\u003eAm. J. Psychiatry\u003c/em\u003e, vol. 154, fasc. 1, pp. 4\u0026ndash;17, gen. 1997.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerber PE, Lynd eLD. \u0026laquo;Selective Serotonin-Reuptake Inhibitor\u0026ndash;Induced Movement Disorders\u0026raquo;, \u003cem\u003eAnn. Pharmacother.\u003c/em\u003e, vol. 32, fasc. 6, pp. 692\u0026ndash;698, giu. 1998, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1345/aph.17302\u003c/span\u003e\u003cspan address=\"10.1345/aph.17302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinto C et al. \u0026laquo;Movement smoothness during a functional mobility task in subjects with Parkinson\u0026rsquo;s disease and freezing of gait - An analysis using inertial measurement units\u0026raquo;, \u003cem\u003eJ. NeuroEngineering Rehabil.\u003c/em\u003e, vol. 16, fasc. 1, pp. 1\u0026ndash;14, 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12984-019-0579-8\u003c/span\u003e\u003cspan address=\"10.1186/s12984-019-0579-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters DM, et al. \u0026laquo;Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review\u0026raquo;. J Neuroeng Rehabil. apr. 2021;18:67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12984-021-00863-x\u003c/span\u003e\u003cspan address=\"10.1186/s12984-021-00863-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. fasc. 1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVancampfort D et al. \u0026laquo;Sedentary behavior and physical activity levels in people with schizophrenia, bipolar disorder and major depressive disorder: a global systematic review and meta-analysis\u0026raquo;, \u003cem\u003eWorld Psychiatry Off. J. World Psychiatr. Assoc. WPA\u003c/em\u003e, vol. 16, fasc. 3, pp. 308\u0026ndash;315, ott. 2017, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/wps.20458\u003c/span\u003e\u003cspan address=\"10.1002/wps.20458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar D, Villarreal DJ, Meuret eAE. \u0026laquo;Walking on the bright side: Associations between affect, depression, and gait\u0026raquo;, \u003cem\u003ePLoS ONE\u003c/em\u003e, vol. 16, fasc. 12 December, pp. 1\u0026ndash;19, 2021, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0260893\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0260893\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatience J, Lai KSP, Russell E, Vasudev A, Montero-Odasso M, Burhan eAM. \u0026laquo;Relationship Between Mood, Thinking, and Walking: A Systematic Review Examining Depressive Symptoms, Executive Function, and Gait\u0026raquo;, \u003cem\u003eAm. J. Geriatr. Psychiatry\u003c/em\u003e, vol. 27, fasc. 12, pp. 1375\u0026ndash;1383, 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jagp.2019.07.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jagp.2019.07.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee CH, Sun TL, Jiang BC, Choi eVH. \u0026laquo;Using wearable accelerometers in a community service context to categorize falling behavior\u0026raquo;, \u003cem\u003eEntropy\u003c/em\u003e, vol. 18, fasc. 7, pp. 1\u0026ndash;14, 2016, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/e18070257\u003c/span\u003e\u003cspan address=\"10.3390/e18070257\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4174631/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4174631/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMajor Depressive Disorder (MDD) is one of the most common psychological disorders. The multiplicity of its clinical patterns, the varieties of symptoms and the different types of clinical evolution generate many diagnostic difficulties. Currently, MDD assessment is performed through the use of assessment scales and interviews with the patient. This highlights the need to adopt methods that can make an objective, rapid and effective diagnosis. Mental disorders and embodiment can alter the brain processes that are related to cognition and therefore to the motor system. As a result, movement analysis by means of wearable sensors is attracting the attention of clinicians as it represents a solution that can be quickly translated into ecological environments.\u003c/p\u003e \u003cp\u003eIn this study we explored the potential of an instrumented movement assessment, targeting the long-term goal of self-administered assessment in ecological settings. Using 4 inertial measurement units (IMUs), we tested parameters that could be predictive of pathology during a timed up and go test. By means of age/sex adjusted logistic regression we identified instrumental parameters significantly discriminating MDD patients and controls. Building on earlier findings in literature for pathology recognition in movement, a particular attention was devoted to parameters concerning movement complexity evaluated by multiscale entropy analysis of signals. Our tests confirmed that complexity parameters can significantly discriminate between patients and controls (Nagelkerke\u0026rsquo;s R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.523).\u003c/p\u003e","manuscriptTitle":"Complexity indexes from instrumented timed-up-and-go test reveal depression-related movement differences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 12:47:33","doi":"10.21203/rs.3.rs-4174631/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b872501d-16f7-4949-9959-edda243c7e78","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-17T21:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 12:47:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4174631","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4174631","identity":"rs-4174631","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}