Sit-To-Walk Strategy Classification Using Hip and Knee Joint Angles at Gait Initiation

Sit-To-Walk Strategy Classification Using Hip and Knee Joint Angles at Gait Initiation | 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 Article Sit-To-Walk Strategy Classification Using Hip and Knee Joint Angles at Gait Initiation Chamalka Kenneth Perera, Alpha Agape Gopalai, Darwin Gouwanda, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2718413/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Oct, 2023 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Sit-to-walk (STW) is categorised by three movement strategies - forward continuation, balance and sit-to-stand-and-walk (SiStW). Literature identified strategies through biomechanical parameters using gold standard laboratory equipment, which is expensive, bulky, and not easily integrated into treatment solutions. As strategy becomes apparent at gait-initiation (GI) and the hip/knee are primary contributors in STW, this study proposes the hip/knee joint angles at GI, as an alternate and standalone method of strategy classification - measurable using wearable sensors. To achieve this, K-means clustering was implemented using three clusters and two feature sets (hip/knee angles); with data from an open access online database (age:21–80 years; n = 10). The results identified forward continuation with the lowest hip/knee extension at GI, followed by balance and then SiStW. From this classification, strategy biomechanics were investigated. The biomechanical parameters (derived in this study) that varied between strategies (P < 0.05) were time, horizontal centre of mass (COM) momentum, braking impulse, centre of pressure (COP) range and velocities, COP-COM separation, hip/knee torque and movement fluency. The derived strategy biomechanics are consistent with literature and validate the classification results. Through strategy classification an individual’s strategy-specific biomechanics can be understood and would aid the design and evaluation of interventions for movement impaired individuals. Physical sciences/Engineering/Biomedical engineering Health sciences/Anatomy/Musculoskeletal system Health sciences/Health care/Quality of life Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Sit-to-walk (STW) is a critical weight-bearing activity of daily living (ADL), with adults performing this task approximately sixty times daily 1 . STW takes place when an individual transitions from a seated position to walking, via standing. This common motion, although seemingly basic, plays a major role in ensuring stability during time critical ADLs. For example, when we rush from a seated position to answer (1) a telephone, (2) a doorbell, or (3) when responding to an emergency. However, literature reports that the ability to execute STW deteriorates with age 2 – 4 . Losing the ability to perform STW safely not only increases fall risk but also results in physical, psychological, and emotional degradation 5 , 6 . Therefore, investigating and understanding the biomechanics, characteristics and execution of STW, is a vital first step in ensuring mobility, independent living and a good quality of life for adults and movement impaired individuals 2 , 4 . At present, there is numerous literature investigating sit-to-stand (SiSt). However, SiSt is merely a subset of STW, because it is normal to assume that an individual will ambulate immediately upon standing. Hence, in STW the end goal is walking, which is more common in daily life, making it a better representation of ADLs 4 . STW is defined as a fluid merging of SiSt and gait, at the point of gait-initiation (GI), where GI is denoted as the heel-off of the swing foot. Subsequently, the STW cycle can be divided into four transitionary phases: (1) flexion-momentum, (2) extension, (3) unloading, and terminating with (4) stance; after which, gait proceeds (Fig. 2 ) 2 , 7 . Literature reports of multiple studies that observed variations in STW biomechanics and executions (in their subject populations), and how these variations were generalised into different STW movement strategies. These identified strategies can be generally divided into three groups (Fig. 1 ): (a) Forward continuation, (b) Balance and (c) Sit-to-stand-and-walk (SiStW). Considering this, Magnan et al., 8 researched on the anteroposterior (AP) ground reaction forces (GRFs), centre of mass (COM) momentum and displacement, with centre of pressure (COP) trajectories, to differentiate between forward continuation and balance strategies in healthy adults. Similarly, Rousanoglou et al., 9 investigated movement speed and duration (fast and preferred speeds), COM velocity and displacement, COP trajectories and the temporal patterns of the STW transition phases, to also distinguish between such strategies. On the other hand, Bestaven et al., 10 and Buckley et al., 2 considered the total COP trajectory, COM momentum, COP-COM separation and step length/velocity in relation to ageing to propose an alternate STW strategy commonly seen in older adults (SiStW). Furthermore, Chandler et al., 11 and Kerr et al., 12 studied the variation in movement fluency during STW, while Jones et al., 13 sought to find consistent biomechanical parameters between different STW strategies. Each STW strategy shows a different execution and can be described by a set of biomechanics, based on the variation in the above biomechanical parameters, investigated throughout literature. As illustrated in Fig. 1 , in forward continuation, a large horizontal COM (hCOM) momentum is generated, to propel the body forwards and upwards, while GI occurs earlier than in the other strategies (closer to seat-off). The feet or base of support (BOS) can be further away from the body (COM), as the generated momentum will carry the individual forwards. In balance, a braking impulse (posterior GRF) occurs to reduce the hCOM momentum generated and maintain quasi-static and postural stability, while rising. GI is delayed and the BOS is closer to the COM, compared to forward continuation 8 , 9 . While in SiStW, a significant braking impulse occurs, with the BOS closest to the COM. This allows the individual to reach an almost upright position, before a delayed GI (compared to forward continuation or balance) 2 . With this, Dehail et al., 14 showed that the quadriceps and hamstrings are the primary muscles involved in STW as they allow for hip and knee extension when rising, while modulating the braking impulse. Therefore, the hip and knee are primary contributors in STW. The above literature highlighted the different executions of STW with their strategy-wise biomechanics, based on the investigated biomechanical parameters. To distinguish between the STW strategies all biomechanical parameters should be considered. However, literature lacked agreement on a single method of strategy classification. Additionally, to derive these biomechanical parameters, gold standard laboratory equipment like motion capture (Mocap) systems or force plates are required. Such equipment is expensive, bulky, cannot be easily integrated into treatment solutions, and not readily accessible in developing regions. Therefore, an alternative method of STW strategy classification using a single, standalone parameter is significant. This would enable strategy identification to be performed outside the laboratory, on wearable devices and in developing areas. Based on the STW strategy executions (Fig. 1 ) and the definition of STW 7 , it is observed that the strategy first becomes apparent only at GI. This is because, before GI an individual begins rising symmetrically (SiSt) and the strategy is not yet distinguishable; however, after GI, as the swing foot moves forward, the chosen strategy is visible. At GI, the complete hCOM momentum and braking impulse generated are observable, along with the COM relative to the BOS, which determine the chosen strategy. Additionally, the hip and knee are primary contributors in STW 14 , with varying degrees of hip and knee joint extension, per strategy, at GI. As such, this study hypothesises that the hip and knee angles can serve as an alternative, distinguishing factor in classifying the STW strategy. Lower limb joint angles for strategy classification are beneficial as they are a standalone biomechanical parameter, independent of upper body movement, and can be easily measured using wearable sensors, in contrast to the biomechanical parameters from literature. Through STW strategy identification, an individual’s chosen STW execution biomechanics and characteristics can be described. This would aid treatment plans and interventions for movement impaired individuals, thus promoting independent living, easier access to ADLs and a better quality of life 15 . In this study, clustering was proposed to classify the strategies into forward continuation, balance and SiStW groups, based on the degree of hip/knee extension at GI. Furthermore, the varying STW strategy execution biomechanics and characteristics were investigated, to understand the strategies and for validation with literature. Methods Experiment details Data from an open access database by Liang et al., 16 performed in the Rehabilitation Research Institute of Singapore, was considered in this study. The data collection was approved by the Nanyang Technological University Institutional Review Board (IRB-2018-04-014), and all subjects provided informed consent before commencement, in accordance with the Declaration of Helsinki. Raw Mocap and force plate data were provided in C3D format, from the NTU Dataverse database 16 . The dataset consisted of ten healthy subjects of Asian ethnicity (weight: 60.6 ± 11.3 kg, height: 166.5 ± 10.9 cm) with a wide age group ranging from 21 to 80 years, inclusive of all three STW strategies. Subjects performed the timed-up-and-go (TUG) test, as detailed in Chen & Chou 17 , with three repetitions, from which STW was obtained. During the TUG test, subjects were asked to stand from a seated position, walk forward for 3m, turn around, walk back, and sit down. Mocap data was obtained through a Qualisys (Sweden) Mocap system, sampling at 200 Hz, while GRF and COP data were obtained using two Kistler (Switzerland) force plates, sampling at 2000 Hz. Data processing First, the raw data was filtered to minimise noise, motion artifacts and for smoothing - using a zero-lag, second order, Butterworth lowpass filter with a cut-off frequency of 5 Hz and 20 Hz for Mocap and force plate data, respectively 18 , 19 . The filter cut-off frequencies were selected by performing a Fast Fourier Transform and observing the 99% occupied signal bandwidth. Additionally, the force plate cut-off frequency was selected to preserve GRF events during GI. Following this, OpenSim 4.2 20,21 was used for biomechanical analysis (see Supplementary Information), with STW being modelled using the Gait2392 Musculoskeletal Model 22 . This model was chosen as only the lower limbs were studied, while still accounting for upper body weight. Scaling was performed to match the subject’s anthropometry to the model, followed by inverse kinematics and inverse dynamics, to compute hip and knee joint angles and torques, respectively 23 . Additionally, COM trajectories and velocities were derived through BodyKinematics analysis, while COP and GRFs were obtained directly from the force plates. Subsequently, these biomechanical parameters were analysed using MATLAB (Mathworks Inc.). Data analysis Joint angle clustering As STW is defined as a fluid merging of SiSt and gait at the point of GI 7 , the three STW strategies become apparent only at GI. After GI, gait begins as the swing foot moves forward at ‘toe-off (TO) Swing’ (Fig. 2 ), with different execution mechanics. Based on the STW strategy executed, the hip and knee joint angles (which are the primary contributors of STW), vary at GI (Fig. 1 ), and thus can be used to identify the strategy. K-means clustering was chosen for STW strategy classification as three distinctly identifiable strategies are known to exist, from literature. It is a fast and established algorithm; that can naturally identify these selected groups, within a numerical dataset with similar characteristics 24 . As STW executions are grouped into three strategies, K-mean clustering was performed using three clusters and two data/feature sets - the hip and knee joint angles at GI 2 , 8 . With this, Synthetic minority oversampling technique (SMOTE) was applied, to equalise sample length for each strategy. The cluster range was visualised using a circle, centred at the cluster centroid and radius equal to the Euclidean distance of the furthest point. Considering this, the strategies were identified based on each cluster centroid’s degree of hip/knee extension at GI. The forward continuation cluster had the lowest hip/knee extension (largest joint angle magnitude) followed by balance (moderate hip/knee extension) and finally SiStW - which had the greatest hip/knee extension (lowest joint angle magnitude), as subjects were almost upright at GI. With this, silhouette analysis (describing cluster cohesion and separation) was performed, and it produced values greater than zero, showing that no data points were wrongly assigned to a cluster 25 . Fuzzy C-means clustering was also performed using the hip and knee joint angles at GI and produced identical results to that from K-means clustering, thus validating the K-means clustering results. Biomechanical parameters A series of kinetic, kinematic and movement fluency parameters were derived, based on the four STW transition phases (Fig. 2 ). These parameters, as illustrated in Fig. 3 , were analysed to understand the biomechanics of each of the three classified STW strategies in this study. The derived strategy-wise biomechanics were consistent with previously reported findings 2 , 4 , 8 – 10 , 12 and can be used to validate the joint angle based strategy classification. The kinematic and kinetic parameters derived in this study were movement duration, hCOM and vertical COM (vCOM) momentum, braking impulse, COP range and velocities, COP-COM separation and joint angles and torques. Movement duration was obtained from the start of flexion-momentum (beginning of phase 1) to the end of stance (completion of phase 4), which terminates with a TO of the stance foot. Movement initiation was found using the first change in vertical GRF 7 , which also corresponds with the start of an anterior increase in hCOM velocity as the trunk flexes forward 8 . Next, the hCOM velocity at seat-off, represented the hCOM momentum generated during flexion-momentum, and the peak vCOM velocity represented the maximum momentum during extension. Additionally, the posterior GRF during flexion-momentum represented the braking impulse, where the area and peak were considered, as a percentage of bodyweight 8 , 9 . Moreover, the AP and mediolateral (ML) COP instantaneous velocities and range were obtained as deviations from the force plate centre, while the AP/ML COP-COM separation was the difference between COM and COP trajectories. Both these quantities were obtained from extension to unloading phases because, during flexion-momentum there is no significant COP displacement as the feet remain on a single force plate, and during stance, the foot leaves the force plate anteriorly, with gait. Furthermore, the hip/knee joint angles and torques derived from OpenSim were found between seat-off and TO of the stance foot, at points of interest (Table 2 ). Joint angles were normalised to 0° when the subject was upright and for joint torque, with respect to the bodyweight. The maximum joint torques (not normalised), occurring around seat-off, were also tabulated. Movement fluency reflects movement mechanical efficiency and comprises three objective measures - hesitation, coordination, and smoothness 11 , 12 . Hesitation describes an uncertain movement and is the maximum percentage drop in hCOM velocity, from the initial peak before seat-off. Coordination describes the synchroneity of joint movements, via the percentage temporal overlap (with respect to total time) between when the hip/knee move into extension (C1 in Fig. 3 ) or flexion (C2 in Fig. 3 ) during the flexion-momentum and unloading phases, respectively. Smoothness describes motion inconsistency and was measured as the total number of inflections in the jerk hCOM signal. Statistical analysis In this work, SPSS Statistics (IBM) was used to conduct the statistical analysis; with tests using an α of 0.05. This analysis was conducted to find if a statistically significant difference existed between the biomechanical parameters (see the above section), for each STW strategy (Fig. 1 ); thus, allowing the biomechanics of each STW strategy to be investigated. The first analysis conducted was to test for normality using the Shapiro-Wilk test, which revealed that the distribution of the dataset was non-parametric. Following this finding, a Kruskal-Wallis H test was selected to find statistical differences between the three STW strategies (independent variable groups) for each biomechanical parameter (dependent variables). Subsequently, for parameters with a statistically significant difference (P < 0.05), a Mann-Whitney U test was performed as a post-hoc test, to find how each biomechanical parameter differed between the three strategies, and their extent. Results Strategy classification Table 1 . Hip and knee joint angle ranges for each STW strategy cluster. Hip and Knee Joint Angle Ranges for STW Strategy Clusters Joint Angle Range (deg) Forward Continuation Balance SiStW Hip Knee Hip Knee Hip Knee Maximum 84.32 76.63 68.70 59.74 41.45 25.89 Minimum 58.04 59.76 36.75 28.47 7.47 5.58 Figure 4 shows the K-means clustering results, which classify the three STW strategies using the hip/knee joint angles at GI, while Table 1 gives their respective joint angle ranges. From this illustration, forward continuation (red) had the lowest hip/knee extension (largest joint angle magnitude), followed by balance (green) and then SiStW (blue), which had the highest level of hip/knee extension as the subjects were almost upright at GI. An overlap exists between forward continuation and balance, showing a level of similarity and allowing them to be grouped together as previously reported by Magnan et al. 8 . Alternatively, SiStW acts as a separate postural conservative strategy which is consistent with the findings of Buckley et al. 2 . Strategy biomechanics Table 2 Results of statistical analysis for kinematic, kinetic and movement fluency parameters Biomechanical Parameters (P < 0.05) for Kruskal-Wallis H Test Mann-Whitney U Test (Post-hoc Testing) Central Tendency and Variability - Median (IQR) Forward & Balance Forward & SiStW Balance & SiStW Forward Balance SiStW Total time (s) P < 0.001 0.003 P < 0.001 0.003 1.41 (0.20) 1.68 (0.38) 2.00 (0.13) Horizontal COM velocity at seat-off (m/s) P < 0.001 0.032 P < 0.001 0.001 0.57 (0.09) 0.46 (0.19) 0.35 (0.15) Peak vertical COM velocity (m/s) 0.893 - - - - - - Braking impulse Area (Ns) P < 0.001 0.005 P < 0.001 0.002 2.40 (2.32) 4.46 (8.82) 15.63 (4.37) Peak (N) 0.025 0.028 0.015 0.488 55.28 (30.15) 98.35 (43.83) 86.04 (15.30) COP range (mm) AP 0.748 - - - - - - ML P < 0.001 0.007 P < 0.001 0.001 84.22 (8.22) 113.62 (41.39) 164.14 (48.04) Instantaneous COP velocity (m/s 2 ) Seat-off AP 0.343 - - - - - - ML 0.368 - - - - - - Gait-initiation AP P < 0.001 0.954 P < 0.001 0.001 399.94 (124.50) 372.48 (219.33) -95.79 (136.69) ML P < 0.001 0.009 P < 0.001 0.011 -257.34 (153.97) -548.54 (282.38) -727.59 (225.19) Toe-off swing foot AP 0.001 0.862 P < 0.001 0.002 343.01 (208.30) 307.61 (223.16) -227.60 (155.65) ML 0.003 0.007 0.003 0.273 -386.48 (230.92) -621.44 (246.14) -711.57 (263.79) COP-COM separation (mm) Seat-off AP 0.001 0.021 0.001 0.043 -185.25 (46.48) -154.54 (21.67) -141.21 (18.11) ML 0.010 0.954 0.038 0.002 281.79 (27.38) 276.92 (19.06) 264.84 (5.69) Gait-initiation AP 0.111 - - - - - - ML 0.679 - - - - - - Toe-off swing foot AP 0.107 - - - - - - ML 0.178 - - - - - - Maximum joint torque (Nm) Hip 0.061 - - - 146.63 (61.77) 133.23 (50.34) 189.32 (47.32) Knee 0.152 - - - 94.58 (29.31) 84.14 (77.66) 139.10 (59.41) Normalized joint torque (Nm/kg) Seat-off Hip 0.492 - - - - - - Knee 0.274 - - - - - - Gait-initiation Hip P < 0.001 0.012 P < 0.001 P < 0.001 1.68 (0.39) 1.13 (0.66) 0.59 (0.24) Knee P < 0.001 0.001 P < 0.001 0.538 0.78 (0.39) 0.29 (0.64) 0.25 (0.19) Toe-off swing foot Hip P < 0.001 0.011 P < 0.001 0.001 1.08 (0.43) 0.72 (0.26) 0.42 (0.05) Knee P < 0.001 0.002 0.024 P < 0.001 1.06 (0.41) 0.70 (0.53) 1.31 (0.17) Toe-off stance foot Hip 0.007 0.003 0.069 0.094 0.61 (0.20) 0.45 (0.16) 0.52 (0.12) Knee 0.558 - - - - - - Movement fluency Hesitation (%) P < 0.001 0.024 P < 0.001 P < 0.001 13.56 (14.89) 26.34 (34.10) 64.74 (12.01) Coordination (%) C1 P < 0.001 0.386 P < 0.001 0.001 -8.16 (2.03) -7.29 (4.04) -3.30 (2.42) C2 P < 0.001 0.47 P < 0.001 0.002 -3.99 (1.47) -3.49 (0.80) -2.51 (0.63) Jerk 0.002 0.155 0.001 0.017 14.00 (2.50) 15.50 (3.50) 18.00 (2.00) Table 2 presents the statistical analysis of the derived biomechanical parameters. STW strategy classification through K-mean clustering (Fig. 4 ), was used when analysing these parameters. From these results, parameters with a statistically significant difference (P < 0.05) between the STW strategies (bolded P-values in Table 2 ), were used to describe the variation in biomechanics, for each individual strategy. This is significant, as understanding STW strategy biomechanics would aid the design and evaluation of treatment plans for movement impaired individuals. Forward continuation From Table 2 , forward continuation recorded values of shortest duration (median: 1.41 s; IQR: 0.20), largest hCOM momentum (median: 0.57 m/s; IQR: 0.09) and lowest braking impulse (median area: 2.40 Ns; IQR: 2.32) amongst the three strategies. This results in a sharp trunk flexion, to rapidly propel the body forwards and upwards, yet requiring steady balance control to perform, without falling. Additionally, the large negative AP COP-COM separation at seat-off (median: -185.25 mm; IQR: 46.48) showed the COM lags the COP. This means the feet/base of support (BOS) is placed further away from the body (Fig. 1 ), while the large forward momentum keeps balance throughout the motion. Thompson et al., 26 showed that COP position and velocities act as balance predictors. Therefore, the lower ML COP range (median: 84.22 mm; IQR: 8.22) seen in forward continuation could show that this strategy was chosen by individuals with good balance control, such as healthy adults, which are consistent with the findings of Magnan et al., 8 and Rousanoglou et al. 9 . Moreover, Table 2 shows that forward continuation required greater hip and knee extension (lift) torque to raise the individual. This is due to the lower degree of joint extension as the individual is in a more crouched position when employing this strategy (Fig. 5 ). Balance From Table 2 , the balance strategy recorded values of longer duration (median: 1.68 s; IQR: 0.38), lower hCOM momentum (median: 0.46 m/s; IQR: 0.19) and a higher braking impulse (median area: 4.46 Ns; IQR: 8.82), compared to forward continuation. The braking impulse reduces forward momentum and allows the individual to focus on stability and postural control, at the cost of speed and efficiency. Additionally, the lower AP COP-COM separation at seat-off (median: -154.54 mm; IQR: 21.67), shows the BOS closer to the COM (Fig. 1 ), allowing the individual to rise while maintaining quasi-static stability. In contrast to forward continuation, the balance strategy had greater ML COP range (median: 113.62 mm; IQR: 41.39) which can indicate a more cautious STW execution 8 . Furthermore, Table 2 recorded lower hip and knee lift torque compared to forward continuation, as the subjects had a greater level of hip and knee extension or were in a less crouched position (Fig. 5 ). Sit-to-stand-and-walk SiStW reported values of longest duration (median: 2.00 s; IQR: 0.13), highest braking impulse (median area: 15.63 Ns; IQR: 4.37) and lowest hCOM momentum (median: 0.35 m/s; IQR: 0.15), compared to the other strategies (Table 2 ). The large braking impulse (resulting in lower hCOM momentum) allows the individual to stand, maintaining quasi-static stability and reach an almost upright position at GI. This is supported by the BOS being closest to the COM as shown by the lowest AP COP-COM separation (median: -141.21 mm; IQR: 18.11). These parameters minimise the fall risk, while maintaining good postural stability. Moreover, SiStW showed the highest ML COP range (median 164.14 mm; IQR: 48.04) and velocities, which may indicate uncertain movements from individuals with less stability and balance control and is consistent with the findings of Buckley et al. 2 . However, tighter ML COP-COM separation at seat-off (median: 264.84 mm; IQR: 5.69) was observed, which keeps the feet/BOS in line with the COM, thus preserving movement stability 2 , 10 . Therefore, SiStW allows individuals to maintain stability and balance during ADLs. Hip and knee torque are progressively lower in SiStW (Table 2 ), as hip/knee extension is greater, and the individual is in a more upright position. As illustrated in Fig. 5 , as the joints extend, the required torque decreases (from forward continuation to balance to SiStW). However, during TO of the swing foot, the knee flexes, as gait begins. Due to the larger knee extension in SiStW, the knee must move through a greater range of motion and higher knee flexion torque is required at this point. Finally, movement fluency significantly differed (P < 0.05) between SiStW with the other strategies (Table 2 ), as similarly observed by Jones et al. 13 . For SiStW, a large braking impulse results in a greater hCOM momentum drop - greater hesitation and lower smoothness 11 , 12 . Moreover, negative values and smaller magnitudes show weaker coordination as the knee moves into extension/flexion just before the hip. This further emphasises that SiStW is employed when an individual is uncertain of their motion, with weak motor control, balance, and stability. Discussion STW is a vital ADL, therefore it is important to investigate and understand its biomechanics. Literature consisted of several studies that reported the variations in STW execution mechanics, which were generalised into three STW movement strategies 2 , 8 – 10 , 12 , 13 . All biomechanical parameters should be considered in strategy identification, hence literature lacked agreement on a single method of strategy classification. Additionally, these parameters required gold standard equipment (Mocap systems or force plates), which is bulky, expensive, and not readily integrated into treatment solutions. Therefore, an alternative method of STW strategy classification using the hip and knee joint angles at GI was proposed. This was chosen as the hip and knee are the primary contributors in STW 14 , while the strategy first becomes observable at GI. Strategy classification is important as it can be directly used to identify an individual’s STW biomechanics, based on their chosen strategy. This understanding would assist the design and evaluation of interventions in occupational therapy, for fall risk or movement impaired individuals; which in turn would improve quality of life. K-means clustering was performed for STW strategy identification using the hip/knee joint angles, and these findings were used to analyse the variation in biomechanics (derived in this study) between each STW strategy (Table 2 ). The observed variation in STW execution biomechanics (for example with hCOM momentum or braking impulse) are consistent with the findings in literature describing the three STW strategies 2 , 8 – 10 , 12 , 13 . This demonstrates correct application of the K-means cluster grouping and thus, validates the use of hip/knee joint angles at GI, as an alternate method of STW strategy classification. From literature, all biomechanical parameters need to be considered to reliably distinguish the STW strategy, however, joint angles are beneficial as they serve as a standalone parameter. The advantage of using joint angles is that they can be easily measured with wearable sensors. This allows it to be integrated into wearable interventions, used outside the laboratory, or even in developing areas that cannot facilitate such expensive laboratory setups. Additionally, lower limb joint angle strategy classification is independent of upper body movements - such as the use of arm swings to generate forward momentum. An individual can employ either strategy at different instances, where the central nervous system selects and executes the best strategy. STW strategies and their biomechanics need to be considered when designing assistive devices, as they must support an individual to correctly perform their chosen strategy. Moreover, strategy classification could aid in the design and evaluation process of assistive devices. For such a device to be representative of STW, it should provide correct/sufficient assistance regardless of the employed strategy. Therefore, through strategy classification the assistive device can be evaluated against each of the three strategies. Only if the device can perform for all three STW strategies, would it encompass the entire STW motion - describing its efficacy, effectiveness, and applicability to STW. On that account, lift assistive devices should consider the strategy-wise torque variation, to ensure correct levels of assistive torque are provided. Forward continuation required the largest extension torque, followed by balance. In contrast, SiStW required less extension torque, but greater knee flexion torque at TO. This result, coupled with lower hCOM momentum, tighter AP and ML COP-COM separation, and a larger braking impulse, showed that SiStW had a lower fall risk during ADLs. Therefore, SiStW is preferred amongst movement impaired individuals, such as older adults 2 , while balance and forward continuation require better postural and balance control as characteristic of healthy adults 8 . A limitation of this study is only considering torque at points of interest and not investigating the overall STW torque profile, per strategy. Furthermore, the study did not investigate arm strategies 4 , asymmetric foot position or the use of walking aids as commonly used in ADLs. This could affect the momentum generated and balance control and will be researched in future studies. Conclusion This study proposed using the hip/knee joint angles at GI, as an alternate method of distinguishing between the three STW strategies - forward continuation, balance and SiStW. Based on this grouping, the strategy-wise biomechanics were derived, and are consistent with existing literature, thus validating this method of strategy identification. The biomechanical parameters governing STW strategies are hCOM momentum, braking impulse, ML COP range, COP-COM separation at seat-off, joint torque and movement fluency. These strategy biomechanics, coupled with strategy classification would aid the evaluation and design of treatment plans and interventions. Failing to do so, would increase fall risk and impede access to ADLs, for movement impaired individuals. Abbreviations ADL: Activity of daily living AP: Anteroposterior COM: Centre of mass COP: Centre of pressure GI: Gait initiation GRF: Ground reaction force hCOM: Horizontal centre of mass IQR: Interquartile range ML: Mediolateral Mocap: Motion capture SiSt: Sit-to-stand SiStW: Sit-to-stand-and-walk SMOTE: Synthetic minority oversampling technique STW: Sit-to-walk TO: Toe-off TUG: Timed-up-and-go vCOM: Vertical centre of mass Declarations Competing interests The authors declare no competing interests. Data availability All datasets analysed during this study are included in the published article by Liang et al., 16 and can be found under the following hyperlink: https://doi.org/10.1038/s41597-020-00627-7 Acknowledgements This work is supported by the Ministry of Higher Education, Malaysia under the project number: FRGS/1/2022/TK07/MUSM/02/2 Author contributions CP and AG conceived the study and the extent of its scope. CP conducted the study under the supervision of AG. The results for the study were obtained by CP, while the interpretation of the results and their respective conclusions were formulated by CP and AG with support from DG and SA. The manuscript was drafted by CP and AG and reviewed by DG, SA and MS. All authors read and approved the final manuscript. References Dall, P. M. & Kerr, A. Frequency of the sit to stand task: An observational study of free-living adults. Appl Ergon 41 , 58–61 (2010). Buckley, T., Pitsikoulis, C., Barthelemy, E. & Hass, C. J. Age impairs sit-to-walk motor performance. J Biomech 42 , 2318–2322 (2009). Robinovitch, S. N. et al. Video capture of the circumstances of falls in elderly people residing in long-term care: an observational study. Lancet 381 , 47–54 (2013). van der Kruk, E. et al. Why do older adults stand-up differently to young adults?: investigation of compensatory movement strategies in sit-to-walk. npj Aging 8 , 1–19 (2022). Pozaic, T., Lindemann, U., Grebe, A.-K. & Stork, W. Sit-to-Stand Transition Reveals Acute Fall Risk in Activities of Daily Living. IEEE J Transl Eng Health Med 4 , 2700211–2700211 (2016). Xie, H., Chen, P. W., Zhao, L., Sun, X. & Jia, X. J. Relationship between activities of daily living and depression among older adults and the quality of life of family caregivers. Frontiers of Nursing 5 , undefined-undefined (2018). Kerr, A., Durward, B. & Kerr, K. M. Defining phases for the sit-to-walk movement. Clin Biomech (Bristol, Avon) 19 , 385–390 (2004). Magnan, A., McFadyen, B. J. & St-Vincent, G. Modification of the sit-to-stand task with the addition of gait initiation. Gait & Posture 4 , 232–241 (1996). Rousanoglou, E. N., Kondilopoulos, N. & Boudolos, K. D. Fast Motion Speed Alters the Sit-to-Walk Spatial and Temporal Pattern in Healthy Young Men. Sports Med Int Open 4 , E77–E84 (2020). Bestaven, E., Petit, J., Robert, B. & Dehail, P. Center of pressure path during Sit-to-Walk tasks in young and elderly humans. Annals of Physical and Rehabilitation Medicine 56 , 644–651 (2013). Chandler, E. A. et al. Investigating the Relationships Between Three Important Functional Tasks Early After Stroke: Movement Characteristics of Sit-To-Stand, Sit-To-Walk, and Walking. Front Neurol 12 , 660383 (2021). Kerr, A., Pomeroy, V. P., Rowe, P. J., Dall, P. & Rafferty, D. Measuring movement fluency during the sit-to-walk task. Gait & Posture 37 , 598–602 (2013). Jones, G. D., James, D. C., Thacker, M. & Green, D. A. Parameters that remain consistent independent of pausing before gait-initiation during normal rise-to-walk behaviour delineated by sit-to-walk and sit-to-stand-and-walk. PLOS ONE 13 , e0205346 (2018). Dehail, P. et al. Kinematic and electromyographic analysis of rising from a chair during a “Sit-to-Walk” task in elderly subjects: Role of strength. Clinical Biomechanics 22 , 1096–1103 (2007). Norman-Gerum, V. & McPhee, J. Comprehensive description of sit-to-stand motions using force and angle data. Journal of Biomechanics 112 , 110046 (2020). Liang, P. et al. An Asian-centric human movement database capturing activities of daily living. Sci Data 7 , (2020). Chen, T. & Chou, L.-S. Effects of Muscle Strength and Balance Control on Sit-to-Walk and Turn Durations in the Timed Up and Go Test. Arch Phys Med Rehabil 98 , 2471–2476 (2017). Crenna, F., Rossi, G. B. & Berardengo, M. Filtering Biomechanical Signals in Movement Analysis. Sensors (Basel) 21 , 4580 (2021). Winter, D. Biomechanics and Motor Control of Human Movement, Fourth Edition. (2009) doi:10.1002/9780470549148.ch5. Delp, S. L. et al. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng 54 , 1940–1950 (2007). Seth, A. et al. OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLOS Computational Biology 14 , e1006223 (2018). OpenSim. Musculoskeletal Models - OpenSim Documentation. https://simtk-confluence.stanford.edu/display/OpenSim/Musculoskeletal+Models (2021). OpenSim. User’s Guide - OpenSim Documentation. https://simtk-confluence.stanford.edu/display/OpenSim/User%27s+Guide (2021). MATLAB-Kmeans. k-means clustering. https://www.mathworks.com/help/stats/kmeans.html (2021). Shutaywi, M. & Kachouie, N. N. Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering. Entropy (Basel) 23 , 759 (2021). Thompson, L. A., Badache, M., Cale, S., Behera, L. & Zhang, N. Balance Performance as Observed by Center-of-Pressure Parameter Characteristics in Male Soccer Athletes and Non-Athletes. Sports (Basel) 5 , 86 (2017). Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.pdf Cite Share Download PDF Status: Published Journal Publication published 03 Oct, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 20 Jul, 2023 Reviews received at journal 19 Jul, 2023 Reviews received at journal 08 Jun, 2023 Reviews received at journal 17 May, 2023 Reviewers agreed at journal 14 May, 2023 Reviewers agreed at journal 08 May, 2023 Reviewers invited by journal 30 Mar, 2023 Editor assigned by journal 30 Mar, 2023 Editor invited by journal 29 Mar, 2023 Submission checks completed at journal 29 Mar, 2023 First submitted to journal 21 Mar, 2023 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. 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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-2718413","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":187485835,"identity":"ff857ac1-8886-4818-9e59-14c8ff212b0a","order_by":0,"name":"Chamalka Kenneth Perera","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chamalka","middleName":"Kenneth","lastName":"Perera","suffix":""},{"id":187485836,"identity":"e03a4d55-6fc6-47a9-a1ad-dc7f42179960","order_by":1,"name":"Alpha Agape Gopalai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYDACdgY2GJPxAZiSAGIefFqYEVqYDUjWwiZBlBb+Zt5jD34w2Nn1z24+VnWj4o68/OwGxgdv23BrkTjMl27Yw5CcPOPOsbTbOWeeGW64c4DZcC4eLQyHecwkeBiYkxlu5Jjdzm07zLhBIoFNmhePFnmgFsk/DPXJ8jfyvxUDtdjPn5HA/hufFgOgFmkehsN2Bjdy2JiBWhIbbiSwMePTYniYL01axuB4guGdY8bSOWcOJ2+4kdgsOeccbi1yx3uPSb6pqLaXu9388HNOxWHb+TOSD354U4bH++AoMGBIbJCAizA24FPPAIs1ewYJAupGwSgYBaNg5AIAS2JREdNbWlAAAAAASUVORK5CYII=","orcid":"","institution":"Monash University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Alpha","middleName":"Agape","lastName":"Gopalai","suffix":""},{"id":187485837,"identity":"a549d819-0be2-4d99-a59c-e9fb2cf8ddd7","order_by":2,"name":"Darwin Gouwanda","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Darwin","middleName":"","lastName":"Gouwanda","suffix":""},{"id":187485838,"identity":"4a835a94-324a-45fe-8e27-9759efca0c5c","order_by":3,"name":"Siti Anom Ahmad","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Siti","middleName":"Anom","lastName":"Ahmad","suffix":""},{"id":187485839,"identity":"d81d7e38-9494-4b8f-9764-44a3f158f708","order_by":4,"name":"Mazatulfazura Sf Binti Salim","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mazatulfazura","middleName":"Sf Binti","lastName":"Salim","suffix":""}],"badges":[],"createdAt":"2023-03-21 12:14:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2718413/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2718413/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-43148-0","type":"published","date":"2023-10-03T15:02:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":35105677,"identity":"25d67ac0-14ae-4ca8-9b95-47d7d95b6e4c","added_by":"auto","created_at":"2023-03-31 14:41:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1336080,"visible":true,"origin":"","legend":"\u003cp\u003eThe three STW movement strategies where \u003cstrong\u003e(a)\u003c/strong\u003e is forward continuation \u003cstrong\u003e(b)\u003c/strong\u003e is balance\u003csup\u003e8,9\u003c/sup\u003e and \u003cstrong\u003e(c)\u003c/strong\u003e is SiStW\u003csup\u003e2\u003c/sup\u003e. The levels of horizontal center of mass (COM) momentum, braking impulse, and the degree of hip and knee extension at gait-initiation (GI) are shown. Additionally, the foot position is given, which affects how close the COM is to the body’s base of support (BOS).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2718413/v1/a155713d2b83b0c8e43b70fc.png"},{"id":35107393,"identity":"0d326b8e-4d91-4a38-9b82-385532aaaf83","added_by":"auto","created_at":"2023-03-31 14:49:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1077124,"visible":true,"origin":"","legend":"\u003cp\u003eThe four transitionary phases of the STW cycle, adapted from Buckley et al.,2. \u0026nbsp;This includes flexion-momentum, extension, unloading and stance. Seat-off occurs at the end of flexion-momentum while gait-initiation (GI) occurs at the end of the extension phase and is denoted by the heel-off of the swing foot. This is followed by a toe-off (TO) of the swing foot, at the end of unloading and TO of the stance foot at the end of the stance phase.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2718413/v1/75c0572e04fde0fffbe4ec9f.png"},{"id":35105679,"identity":"146bbcfe-5bfa-4605-950d-4d5c1fb02bdf","added_by":"auto","created_at":"2023-03-31 14:41:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7136441,"visible":true,"origin":"","legend":"\u003cp\u003eTime series plots of the biomechanical parameters, from a sample subject. The plots include the horizontal centre of mass (COM) and vertical COM velocities, anteroposterior (AP) and mediolateral (ML) center of pressure (COP) trajectories and velocities, AP and ML COP-COM separation, braking impulse and measures of movement fluency - hesitation, coordination (extension occurs at C1 and flexion occurs at C2), and smoothness. The STW events and phases are denoted by vertical dotted lines, where seat-off, gait-initiation (GI), toe-off (TO) of the swing foot and TO of the stance foot, are represented by the end of flexion-momentum (red), extension (blue), unloading (green), and stance (magenta) phases, respectively.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2718413/v1/3160072d4f1593271fb594c8.png"},{"id":35107394,"identity":"75894749-aa14-4abf-b5f1-3d7edc4bd068","added_by":"auto","created_at":"2023-03-31 14:49:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38652,"visible":true,"origin":"","legend":"\u003cp\u003eK-means clustering of STW strategies using the hip and knee joint angles at gait-initiation (GI). Forward continuation (red) had the lowest hip and knee extension (largest joint angle magnitude), followed by balance (green) and then SiStW (blue), which had the greatest hip and knee extension at GI (smallest joint angle magnitude).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2718413/v1/8f4abe9278d05a696a57d91e.png"},{"id":35105681,"identity":"6601e401-23e5-45bd-897c-3e8486f3315d","added_by":"auto","created_at":"2023-03-31 14:41:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5354431,"visible":true,"origin":"","legend":"\u003cp\u003eHip and knee joint angle and torque plots, from selected subjects with respect to the three STW strategies. The STW events and phases are denoted by vertical dotted lines, where seat-off, gait-initiation (GI), toe-off (TO) of the swing foot and TO of the stance foot, are represented by the end of flexion-momentum (red), extension (blue), unloading (green), and stance (magenta) phases, respectively.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2718413/v1/02c99e64457512e6082f5eb9.jpg"},{"id":44301946,"identity":"9bd10ae8-11e0-4c87-a2b8-3b06ba8a1a2a","added_by":"auto","created_at":"2023-10-09 15:08:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1304640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2718413/v1/84633778-7291-424c-9de5-e81321743b09.pdf"},{"id":35105682,"identity":"1030fcb4-d93d-46ae-9f8a-4e77d0cd334d","added_by":"auto","created_at":"2023-03-31 14:41:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":349957,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2718413/v1/4fe8ec0e97f2917ddf8c8e42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sit-To-Walk Strategy Classification Using Hip and Knee Joint Angles at Gait Initiation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSit-to-walk (STW) is a critical weight-bearing activity of daily living (ADL), with adults performing this task approximately sixty times daily\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. STW takes place when an individual transitions from a seated position to walking, via standing. This common motion, although seemingly basic, plays a major role in ensuring stability during time critical ADLs. For example, when we rush from a seated position to answer (1) a telephone, (2) a doorbell, or (3) when responding to an emergency. However, literature reports that the ability to execute STW deteriorates with age\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Losing the ability to perform STW safely not only increases fall risk but also results in physical, psychological, and emotional degradation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, investigating and understanding the biomechanics, characteristics and execution of STW, is a vital first step in ensuring mobility, independent living and a good quality of life for adults and movement impaired individuals\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt present, there is numerous literature investigating sit-to-stand (SiSt). However, SiSt is merely a subset of STW, because it is normal to assume that an individual will ambulate immediately upon standing. Hence, in STW the end goal is walking, which is more common in daily life, making it a better representation of ADLs\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. STW is defined as a fluid merging of SiSt and gait, at the point of gait-initiation (GI), where GI is denoted as the heel-off of the swing foot. Subsequently, the STW cycle can be divided into four transitionary phases: (1) flexion-momentum, (2) extension, (3) unloading, and terminating with (4) stance; after which, gait proceeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLiterature reports of multiple studies that observed variations in STW biomechanics and executions (in their subject populations), and how these variations were generalised into different STW movement strategies. These identified strategies can be generally divided into three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e): (a) Forward continuation, (b) Balance and (c) Sit-to-stand-and-walk (SiStW). Considering this, Magnan et al.,\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e researched on the anteroposterior (AP) ground reaction forces (GRFs), centre of mass (COM) momentum and displacement, with centre of pressure (COP) trajectories, to differentiate between forward continuation and balance strategies in healthy adults. Similarly, Rousanoglou et al.,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e investigated movement speed and duration (fast and preferred speeds), COM velocity and displacement, COP trajectories and the temporal patterns of the STW transition phases, to also distinguish between such strategies. On the other hand, Bestaven et al.,\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and Buckley et al.,\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e considered the total COP trajectory, COM momentum, COP-COM separation and step length/velocity in relation to ageing to propose an alternate STW strategy commonly seen in older adults (SiStW). Furthermore, Chandler et al.,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and Kerr et al.,\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e studied the variation in movement fluency during STW, while Jones et al.,\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e sought to find consistent biomechanical parameters between different STW strategies. Each STW strategy shows a different execution and can be described by a set of biomechanics, based on the variation in the above biomechanical parameters, investigated throughout literature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, in forward continuation, a large horizontal COM (hCOM) momentum is generated, to propel the body forwards and upwards, while GI occurs earlier than in the other strategies (closer to seat-off). The feet or base of support (BOS) can be further away from the body (COM), as the generated momentum will carry the individual forwards. In balance, a braking impulse (posterior GRF) occurs to reduce the hCOM momentum generated and maintain quasi-static and postural stability, while rising. GI is delayed and the BOS is closer to the COM, compared to forward continuation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. While in SiStW, a significant braking impulse occurs, with the BOS closest to the COM. This allows the individual to reach an almost upright position, before a delayed GI (compared to forward continuation or balance)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. With this, Dehail et al.,\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e showed that the quadriceps and hamstrings are the primary muscles involved in STW as they allow for hip and knee extension when rising, while modulating the braking impulse. Therefore, the hip and knee are primary contributors in STW.\u003c/p\u003e \u003cp\u003eThe above literature highlighted the different executions of STW with their strategy-wise biomechanics, based on the investigated biomechanical parameters. To distinguish between the STW strategies all biomechanical parameters should be considered. However, literature lacked agreement on a single method of strategy classification. Additionally, to derive these biomechanical parameters, gold standard laboratory equipment like motion capture (Mocap) systems or force plates are required. Such equipment is expensive, bulky, cannot be easily integrated into treatment solutions, and not readily accessible in developing regions. Therefore, an alternative method of STW strategy classification using a single, standalone parameter is significant. This would enable strategy identification to be performed outside the laboratory, on wearable devices and in developing areas.\u003c/p\u003e \u003cp\u003eBased on the STW strategy executions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the definition of STW\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, it is observed that the strategy first becomes apparent only at GI. This is because, before GI an individual begins rising symmetrically (SiSt) and the strategy is not yet distinguishable; however, after GI, as the swing foot moves forward, the chosen strategy is visible. At GI, the complete hCOM momentum and braking impulse generated are observable, along with the COM relative to the BOS, which determine the chosen strategy. Additionally, the hip and knee are primary contributors in STW\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, with varying degrees of hip and knee joint extension, per strategy, at GI. As such, this study hypothesises that the hip and knee angles can serve as an alternative, distinguishing factor in classifying the STW strategy. Lower limb joint angles for strategy classification are beneficial as they are a standalone biomechanical parameter, independent of upper body movement, and can be easily measured using wearable sensors, in contrast to the biomechanical parameters from literature. Through STW strategy identification, an individual\u0026rsquo;s chosen STW execution biomechanics and characteristics can be described. This would aid treatment plans and interventions for movement impaired individuals, thus promoting independent living, easier access to ADLs and a better quality of life\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In this study, clustering was proposed to classify the strategies into forward continuation, balance and SiStW groups, based on the degree of hip/knee extension at GI. Furthermore, the varying STW strategy execution biomechanics and characteristics were investigated, to understand the strategies and for validation with literature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperiment details\u003c/h2\u003e \u003cp\u003eData from an open access database by Liang et al.,\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e performed in the Rehabilitation Research Institute of Singapore, was considered in this study. The data collection was approved by the Nanyang Technological University Institutional Review Board (IRB-2018-04-014), and all subjects provided informed consent before commencement, in accordance with the Declaration of Helsinki. Raw Mocap and force plate data were provided in C3D format, from the NTU Dataverse database\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The dataset consisted of ten healthy subjects of Asian ethnicity (weight: 60.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3 kg, height: 166.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 cm) with a wide age group ranging from 21 to 80 years, inclusive of all three STW strategies. Subjects performed the timed-up-and-go (TUG) test, as detailed in Chen \u0026amp; Chou\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, with three repetitions, from which STW was obtained. During the TUG test, subjects were asked to stand from a seated position, walk forward for 3m, turn around, walk back, and sit down. Mocap data was obtained through a Qualisys (Sweden) Mocap system, sampling at 200 Hz, while GRF and COP data were obtained using two Kistler (Switzerland) force plates, sampling at 2000 Hz.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData processing\u003c/h2\u003e \u003cp\u003eFirst, the raw data was filtered to minimise noise, motion artifacts and for smoothing - using a zero-lag, second order, Butterworth lowpass filter with a cut-off frequency of 5 Hz and 20 Hz for Mocap and force plate data, respectively\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The filter cut-off frequencies were selected by performing a Fast Fourier Transform and observing the 99% occupied signal bandwidth. Additionally, the force plate cut-off frequency was selected to preserve GRF events during GI.\u003c/p\u003e \u003cp\u003eFollowing this, OpenSim 4.2\u003csup\u003e20,21\u003c/sup\u003e was used for biomechanical analysis (see Supplementary Information), with STW being modelled using the Gait2392 Musculoskeletal Model\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This model was chosen as only the lower limbs were studied, while still accounting for upper body weight. Scaling was performed to match the subject\u0026rsquo;s anthropometry to the model, followed by inverse kinematics and inverse dynamics, to compute hip and knee joint angles and torques, respectively\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Additionally, COM trajectories and velocities were derived through BodyKinematics analysis, while COP and GRFs were obtained directly from the force plates. Subsequently, these biomechanical parameters were analysed using MATLAB (Mathworks Inc.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eJoint angle clustering\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs STW is defined as a fluid merging of SiSt and gait at the point of GI\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, the three STW strategies become apparent only at GI. After GI, gait begins as the swing foot moves forward at \u0026lsquo;toe-off (TO) Swing\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with different execution mechanics. Based on the STW strategy executed, the hip and knee joint angles (which are the primary contributors of STW), vary at GI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and thus can be used to identify the strategy.\u003c/p\u003e \u003cp\u003eK-means clustering was chosen for STW strategy classification as three distinctly identifiable strategies are known to exist, from literature. It is a fast and established algorithm; that can naturally identify these selected groups, within a numerical dataset with similar characteristics\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. As STW executions are grouped into three strategies, K-mean clustering was performed using three clusters and two data/feature sets - the hip and knee joint angles at GI\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. With this, Synthetic minority oversampling technique (SMOTE) was applied, to equalise sample length for each strategy. The cluster range was visualised using a circle, centred at the cluster centroid and radius equal to the Euclidean distance of the furthest point.\u003c/p\u003e \u003cp\u003eConsidering this, the strategies were identified based on each cluster centroid\u0026rsquo;s degree of hip/knee extension at GI. The forward continuation cluster had the lowest hip/knee extension (largest joint angle magnitude) followed by balance (moderate hip/knee extension) and finally SiStW - which had the greatest hip/knee extension (lowest joint angle magnitude), as subjects were almost upright at GI. With this, silhouette analysis (describing cluster cohesion and separation) was performed, and it produced values greater than zero, showing that no data points were wrongly assigned to a cluster\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Fuzzy C-means clustering was also performed using the hip and knee joint angles at GI and produced identical results to that from K-means clustering, thus validating the K-means clustering results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eBiomechanical parameters\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA series of kinetic, kinematic and movement fluency parameters were derived, based on the four STW transition phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These parameters, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, were analysed to understand the biomechanics of each of the three classified STW strategies in this study. The derived strategy-wise biomechanics were consistent with previously reported findings\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and can be used to validate the joint angle based strategy classification.\u003c/p\u003e \u003cp\u003eThe kinematic and kinetic parameters derived in this study were movement duration, hCOM and vertical COM (vCOM) momentum, braking impulse, COP range and velocities, COP-COM separation and joint angles and torques. Movement duration was obtained from the start of flexion-momentum (beginning of phase 1) to the end of stance (completion of phase 4), which terminates with a TO of the stance foot. Movement initiation was found using the first change in vertical GRF\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, which also corresponds with the start of an anterior increase in hCOM velocity as the trunk flexes forward\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Next, the hCOM velocity at seat-off, represented the hCOM momentum generated during flexion-momentum, and the peak vCOM velocity represented the maximum momentum during extension. Additionally, the posterior GRF during flexion-momentum represented the braking impulse, where the area and peak were considered, as a percentage of bodyweight\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Moreover, the AP and mediolateral (ML) COP instantaneous velocities and range were obtained as deviations from the force plate centre, while the AP/ML COP-COM separation was the difference between COM and COP trajectories. Both these quantities were obtained from extension to unloading phases because, during flexion-momentum there is no significant COP displacement as the feet remain on a single force plate, and during stance, the foot leaves the force plate anteriorly, with gait. Furthermore, the hip/knee joint angles and torques derived from OpenSim were found between seat-off and TO of the stance foot, at points of interest (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Joint angles were normalised to 0\u0026deg; when the subject was upright and for joint torque, with respect to the bodyweight. The maximum joint torques (not normalised), occurring around seat-off, were also tabulated.\u003c/p\u003e \u003cp\u003eMovement fluency reflects movement mechanical efficiency and comprises three objective measures - hesitation, coordination, and smoothness\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Hesitation describes an uncertain movement and is the maximum percentage drop in hCOM velocity, from the initial peak before seat-off. Coordination describes the synchroneity of joint movements, via the percentage temporal overlap (with respect to total time) between when the hip/knee move into extension (C1 in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) or flexion (C2 in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) during the flexion-momentum and unloading phases, respectively. Smoothness describes motion inconsistency and was measured as the total number of inflections in the jerk hCOM signal.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this work, SPSS Statistics (IBM) was used to conduct the statistical analysis; with tests using an α of 0.05. This analysis was conducted to find if a statistically significant difference existed between the biomechanical parameters (see the above section), for each STW strategy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e); thus, allowing the biomechanics of each STW strategy to be investigated. The first analysis conducted was to test for normality using the Shapiro-Wilk test, which revealed that the distribution of the dataset was non-parametric. Following this finding, a Kruskal-Wallis H test was selected to find statistical differences between the three STW strategies (independent variable groups) for each biomechanical parameter (dependent variables). Subsequently, for parameters with a statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a Mann-Whitney U test was performed as a post-hoc test, to find how each biomechanical parameter differed between the three strategies, and their extent.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003eStrategy classification\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Hip and knee joint angle ranges for each STW strategy cluster.\u003c/p\u003e\n \u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"515\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHip and Knee Joint Angle Ranges for STW Strategy Clusters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"29.182879377431906%\"\u003e\n \u003cp\u003e\u003cstrong\u003eJoint Angle Range (deg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"28.01556420233463%\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward Continuation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.40077821011673%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBalance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"21.40077821011673%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSiStW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.78021978021978%\"\u003e\n \u003cp\u003eHip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.78021978021978%\"\u003e\n \u003cp\u003eKnee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003eHip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003eKnee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003eHip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10989010989011%\"\u003e\n \u003cp\u003eKnee\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.182879377431906%\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.007782101167315%\"\u003e\n \u003cp\u003e84.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.007782101167315%\"\u003e\n \u003cp\u003e76.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.700389105058365%\"\u003e\n \u003cp\u003e68.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.700389105058365%\"\u003e\n \u003cp\u003e59.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.700389105058365%\"\u003e\n \u003cp\u003e41.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.700389105058365%\"\u003e\n \u003cp\u003e25.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.182879377431906%\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.007782101167315%\"\u003e\n \u003cp\u003e58.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.007782101167315%\"\u003e\n \u003cp\u003e59.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.700389105058365%\"\u003e\n \u003cp\u003e36.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.700389105058365%\"\u003e\n \u003cp\u003e28.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.700389105058365%\"\u003e\n \u003cp\u003e7.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.700389105058365%\"\u003e\n \u003cp\u003e5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the K-means clustering results, which classify the three STW strategies using the hip/knee joint angles at GI, while Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e gives their respective joint angle ranges. From this illustration, forward continuation (red) had the lowest hip/knee extension (largest joint angle magnitude), followed by balance (green) and then SiStW (blue), which had the highest level of hip/knee extension as the subjects were almost upright at GI. An overlap exists between forward continuation and balance, showing a level of similarity and allowing them to be grouped together as previously reported by Magnan et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Alternatively, SiStW acts as a separate postural conservative strategy which is consistent with the findings of Buckley et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003eStrategy biomechanics\u003c/h2\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab2\" style=\"margin-right: calc(0%); width: 100%;\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of statistical analysis for kinematic, kinetic and movement fluency parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\" rowspan=\"2\" style=\"width: 29.8272%;\"\u003e\n \u003cp\u003eBiomechanical Parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for Kruskal-Wallis H Test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" style=\"width: 24.0542%;\"\u003e\n \u003cp\u003eMann-Whitney U Test (Post-hoc Testing)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" style=\"width: 22.8514%;\"\u003e\n \u003cp\u003eCentral Tendency and Variability - Median (IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003eForward \u0026amp; Balance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003eForward \u0026amp; SiStW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003eBalance \u0026amp; SiStW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003eBalance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003eSiStW\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 29.8272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal time (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e1.41 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e1.68 (0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e2.00 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 29.8272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHorizontal COM velocity at seat-off (m/s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e0.57 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e0.46 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e0.35 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 29.8272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak vertical COM velocity (m/s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\" style=\"width: 24.7758%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBraking impulse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (Ns)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e2.40 (2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e4.46 (8.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e15.63 (4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e55.28 (30.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e98.35 (43.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e86.04 (15.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\" style=\"width: 24.7758%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOP range (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eML\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e84.22 (8.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e113.62 (41.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e164.14 (48.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\" style=\"width: 12.869%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstantaneous COP velocity (m/s\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeat-off\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eML\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGait-initiation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e399.94 (124.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e372.48 (219.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-95.79 (136.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eML\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-257.34 (153.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-548.54 (282.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-727.59 (225.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eToe-off swing foot\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e343.01 (208.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e307.61 (223.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-227.60 (155.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eML\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-386.48 (230.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-621.44 (246.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-711.57 (263.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\" style=\"width: 12.869%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOP-COM separation (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeat-off\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-185.25 (46.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-154.54 (21.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-141.21 (18.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eML\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e281.79 (27.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e276.92 (19.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e264.84 (5.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGait-initiation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eML\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eToe-off swing foot\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eML\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\" style=\"width: 24.7758%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum joint torque (Nm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHip\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e146.63 (61.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e133.23 (50.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e189.32 (47.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnee\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e94.58 (29.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e84.14 (77.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e139.10 (59.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\" style=\"width: 12.869%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormalized joint torque (Nm/kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeat-off\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHip\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnee\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGait-initiation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHip\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e1.68 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e1.13 (0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e0.59 (0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnee\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e0.78 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e0.29 (0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e0.25 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eToe-off swing foot\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHip\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e1.08 (0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e0.72 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e0.42 (0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnee\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e1.06 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e0.70 (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e1.31 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eToe-off stance foot\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHip\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e0.61 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e0.45 (0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e0.52 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnee\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\" style=\"width: 12.869%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMovement fluency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHesitation (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e13.56 (14.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e26.34 (34.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e64.74 (12.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoordination (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-8.16 (2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-7.29 (4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-3.30 (2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e-3.99 (1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e-3.49 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e-2.51 (0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 11.6663%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJerk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.9311%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 9.0203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.1784%;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.0581%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.3365%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.6973%;\"\u003e\n \u003cp\u003e14.00 (2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.5771%;\"\u003e\n \u003cp\u003e15.50 (3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.096%;\"\u003e\n \u003cp\u003e18.00 (2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the statistical analysis of the derived biomechanical parameters. STW strategy classification through K-mean clustering (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), was used when analysing these parameters. From these results, parameters with a statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the STW strategies (bolded P-values in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), were used to describe the variation in biomechanics, for each individual strategy. This is significant, as understanding STW strategy biomechanics would aid the design and evaluation of treatment plans for movement impaired individuals.\u003c/p\u003e\n \u003cdiv class=\"Section3\" id=\"Sec12\"\u003e\n \u003ch2\u003eForward continuation\u003c/h2\u003e\n \u003cp\u003eFrom Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, forward continuation recorded values of shortest duration (median: 1.41 s; IQR: 0.20), largest hCOM momentum (median: 0.57 m/s; IQR: 0.09) and lowest braking impulse (median area: 2.40 Ns; IQR: 2.32) amongst the three strategies. This results in a sharp trunk flexion, to rapidly propel the body forwards and upwards, yet requiring steady balance control to perform, without falling. Additionally, the large negative AP COP-COM separation at seat-off (median: -185.25 mm; IQR: 46.48) showed the COM lags the COP. This means the feet/base of support (BOS) is placed further away from the body (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), while the large forward momentum keeps balance throughout the motion.\u003c/p\u003e\n \u003cp\u003eThompson et al.,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e showed that COP position and velocities act as balance predictors. Therefore, the lower ML COP range (median: 84.22 mm; IQR: 8.22) seen in forward continuation could show that this strategy was chosen by individuals with good balance control, such as healthy adults, which are consistent with the findings of Magnan et al.,\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and Rousanoglou et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Moreover, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows that forward continuation required greater hip and knee extension (lift) torque to raise the individual. This is due to the lower degree of joint extension as the individual is in a more crouched position when employing this strategy (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec13\"\u003e\n \u003ch2\u003eBalance\u003c/h2\u003e\n \u003cp\u003eFrom Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the balance strategy recorded values of longer duration (median: 1.68 s; IQR: 0.38), lower hCOM momentum (median: 0.46 m/s; IQR: 0.19) and a higher braking impulse (median area: 4.46 Ns; IQR: 8.82), compared to forward continuation. The braking impulse reduces forward momentum and allows the individual to focus on stability and postural control, at the cost of speed and efficiency. Additionally, the lower AP COP-COM separation at seat-off (median: -154.54 mm; IQR: 21.67), shows the BOS closer to the COM (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), allowing the individual to rise while maintaining quasi-static stability.\u003c/p\u003e\n \u003cp\u003eIn contrast to forward continuation, the balance strategy had greater ML COP range (median: 113.62 mm; IQR: 41.39) which can indicate a more cautious STW execution\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Furthermore, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e recorded lower hip and knee lift torque compared to forward continuation, as the subjects had a greater level of hip and knee extension or were in a less crouched position (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec14\"\u003e\n \u003ch2\u003eSit-to-stand-and-walk\u003c/h2\u003e\n \u003cp\u003eSiStW reported values of longest duration (median: 2.00 s; IQR: 0.13), highest braking impulse (median area: 15.63 Ns; IQR: 4.37) and lowest hCOM momentum (median: 0.35 m/s; IQR: 0.15), compared to the other strategies (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The large braking impulse (resulting in lower hCOM momentum) allows the individual to stand, maintaining quasi-static stability and reach an almost upright position at GI. This is supported by the BOS being closest to the COM as shown by the lowest AP COP-COM separation (median: -141.21 mm; IQR: 18.11). These parameters minimise the fall risk, while maintaining good postural stability. Moreover, SiStW showed the highest ML COP range (median 164.14 mm; IQR: 48.04) and velocities, which may indicate uncertain movements from individuals with less stability and balance control and is consistent with the findings of Buckley et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, tighter ML COP-COM separation at seat-off (median: 264.84 mm; IQR: 5.69) was observed, which keeps the feet/BOS in line with the COM, thus preserving movement stability\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, SiStW allows individuals to maintain stability and balance during ADLs.\u003c/p\u003e\n \u003cp\u003eHip and knee torque are progressively lower in SiStW (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), as hip/knee extension is greater, and the individual is in a more upright position. As illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, as the joints extend, the required torque decreases (from forward continuation to balance to SiStW). However, during TO of the swing foot, the knee flexes, as gait begins. Due to the larger knee extension in SiStW, the knee must move through a greater range of motion and higher knee flexion torque is required at this point.\u003c/p\u003e\n \u003cp\u003eFinally, movement fluency significantly differed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between SiStW with the other strategies (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), as similarly observed by Jones et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. For SiStW, a large braking impulse results in a greater hCOM momentum drop - greater hesitation and lower smoothness\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Moreover, negative values and smaller magnitudes show weaker coordination as the knee moves into extension/flexion just before the hip. This further emphasises that SiStW is employed when an individual is uncertain of their motion, with weak motor control, balance, and stability.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSTW is a vital ADL, therefore it is important to investigate and understand its biomechanics. Literature consisted of several studies that reported the variations in STW execution mechanics, which were generalised into three STW movement strategies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. All biomechanical parameters should be considered in strategy identification, hence literature lacked agreement on a single method of strategy classification. Additionally, these parameters required gold standard equipment (Mocap systems or force plates), which is bulky, expensive, and not readily integrated into treatment solutions. Therefore, an alternative method of STW strategy classification using the hip and knee joint angles at GI was proposed. This was chosen as the hip and knee are the primary contributors in STW\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, while the strategy first becomes observable at GI. Strategy classification is important as it can be directly used to identify an individual\u0026rsquo;s STW biomechanics, based on their chosen strategy. This understanding would assist the design and evaluation of interventions in occupational therapy, for fall risk or movement impaired individuals; which in turn would improve quality of life.\u003c/p\u003e \u003cp\u003eK-means clustering was performed for STW strategy identification using the hip/knee joint angles, and these findings were used to analyse the variation in biomechanics (derived in this study) between each STW strategy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The observed variation in STW execution biomechanics (for example with hCOM momentum or braking impulse) are consistent with the findings in literature describing the three STW strategies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This demonstrates correct application of the K-means cluster grouping and thus, validates the use of hip/knee joint angles at GI, as an alternate method of STW strategy classification.\u003c/p\u003e \u003cp\u003eFrom literature, all biomechanical parameters need to be considered to reliably distinguish the STW strategy, however, joint angles are beneficial as they serve as a standalone parameter. The advantage of using joint angles is that they can be easily measured with wearable sensors. This allows it to be integrated into wearable interventions, used outside the laboratory, or even in developing areas that cannot facilitate such expensive laboratory setups. Additionally, lower limb joint angle strategy classification is independent of upper body movements - such as the use of arm swings to generate forward momentum.\u003c/p\u003e \u003cp\u003eAn individual can employ either strategy at different instances, where the central nervous system selects and executes the best strategy. STW strategies and their biomechanics need to be considered when designing assistive devices, as they must support an individual to correctly perform their chosen strategy. Moreover, strategy classification could aid in the design and evaluation process of assistive devices. For such a device to be representative of STW, it should provide correct/sufficient assistance regardless of the employed strategy. Therefore, through strategy classification the assistive device can be evaluated against each of the three strategies. Only if the device can perform for all three STW strategies, would it encompass the entire STW motion - describing its efficacy, effectiveness, and applicability to STW.\u003c/p\u003e \u003cp\u003eOn that account, lift assistive devices should consider the strategy-wise torque variation, to ensure correct levels of assistive torque are provided. Forward continuation required the largest extension torque, followed by balance. In contrast, SiStW required less extension torque, but greater knee flexion torque at TO. This result, coupled with lower hCOM momentum, tighter AP and ML COP-COM separation, and a larger braking impulse, showed that SiStW had a lower fall risk during ADLs. Therefore, SiStW is preferred amongst movement impaired individuals, such as older adults\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, while balance and forward continuation require better postural and balance control as characteristic of healthy adults\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA limitation of this study is only considering torque at points of interest and not investigating the overall STW torque profile, per strategy. Furthermore, the study did not investigate arm strategies\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, asymmetric foot position or the use of walking aids as commonly used in ADLs. This could affect the momentum generated and balance control and will be researched in future studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study proposed using the hip/knee joint angles at GI, as an alternate method of distinguishing between the three STW strategies - forward continuation, balance and SiStW. Based on this grouping, the strategy-wise biomechanics were derived, and are consistent with existing literature, thus validating this method of strategy identification. The biomechanical parameters governing STW strategies are hCOM momentum, braking impulse, ML COP range, COP-COM separation at seat-off, joint torque and movement fluency. These strategy biomechanics, coupled with strategy classification would aid the evaluation and design of treatment plans and interventions. Failing to do so, would increase fall risk and impede access to ADLs, for movement impaired individuals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eADL:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eActivity of daily living\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eAP:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eAnteroposterior\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eCOM:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eCentre of mass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eCOP:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eCentre of pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eGI:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eGait initiation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eGRF:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eGround reaction force\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003ehCOM:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eHorizontal centre of mass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eIQR:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eInterquartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eML:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eMediolateral\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eMocap:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eMotion capture\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eSiSt:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eSit-to-stand\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eSiStW:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eSit-to-stand-and-walk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eSMOTE:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eSynthetic minority oversampling technique\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eSTW:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eSit-to-walk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eTO:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eToe-off\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003eTUG:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eTimed-up-and-go\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.010416666666668%\"\u003e\n \u003cp\u003evCOM:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.98958333333333%\"\u003e\n \u003cp\u003eVertical centre of mass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eAll datasets analysed during this study are included in the published article by Liang et al.,\u003csup\u003e16\u003c/sup\u003e and can be found under the following hyperlink: https://doi.org/10.1038/s41597-020-00627-7\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work is supported by the Ministry of Higher Education, Malaysia under the project number: FRGS/1/2022/TK07/MUSM/02/2\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eCP and AG conceived the study and the extent of its scope. CP conducted the study under the supervision of AG. The results for the study were obtained by CP, while the interpretation of the results and their respective conclusions were formulated by CP and AG with support from DG and SA. The manuscript was drafted by CP and AG and reviewed by DG, SA and MS. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDall, P. M. \u0026amp; Kerr, A. Frequency of the sit to stand task: An observational study of free-living adults. \u003cem\u003eAppl Ergon\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 58\u0026ndash;61 (2010).\u003c/li\u003e\n\u003cli\u003eBuckley, T., Pitsikoulis, C., Barthelemy, E. \u0026amp; Hass, C. J. Age impairs sit-to-walk motor performance. \u003cem\u003eJ Biomech\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 2318\u0026ndash;2322 (2009).\u003c/li\u003e\n\u003cli\u003eRobinovitch, S. N. \u003cem\u003eet al.\u003c/em\u003e Video capture of the circumstances of falls in elderly people residing in long-term care: an observational study. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e381\u003c/strong\u003e, 47\u0026ndash;54 (2013).\u003c/li\u003e\n\u003cli\u003evan der Kruk, E. \u003cem\u003eet al.\u003c/em\u003e Why do older adults stand-up differently to young adults?: investigation of compensatory movement strategies in sit-to-walk. \u003cem\u003enpj Aging\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 1\u0026ndash;19 (2022).\u003c/li\u003e\n\u003cli\u003ePozaic, T., Lindemann, U., Grebe, A.-K. \u0026amp; Stork, W. Sit-to-Stand Transition Reveals Acute Fall Risk in Activities of Daily Living. \u003cem\u003eIEEE J Transl Eng Health Med\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 2700211\u0026ndash;2700211 (2016).\u003c/li\u003e\n\u003cli\u003eXie, H., Chen, P. W., Zhao, L., Sun, X. \u0026amp; Jia, X. J. Relationship between activities of daily living and depression among older adults and the quality of life of family caregivers. \u003cem\u003eFrontiers of Nursing\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, undefined-undefined (2018).\u003c/li\u003e\n\u003cli\u003eKerr, A., Durward, B. \u0026amp; Kerr, K. M. Defining phases for the sit-to-walk movement. \u003cem\u003eClin Biomech (Bristol, Avon)\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 385\u0026ndash;390 (2004).\u003c/li\u003e\n\u003cli\u003eMagnan, A., McFadyen, B. J. \u0026amp; St-Vincent, G. Modification of the sit-to-stand task with the addition of gait initiation. \u003cem\u003eGait \u0026amp; Posture\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 232\u0026ndash;241 (1996).\u003c/li\u003e\n\u003cli\u003eRousanoglou, E. N., Kondilopoulos, N. \u0026amp; Boudolos, K. D. Fast Motion Speed Alters the Sit-to-Walk Spatial and Temporal Pattern in Healthy Young Men. \u003cem\u003eSports Med Int Open\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, E77\u0026ndash;E84 (2020).\u003c/li\u003e\n\u003cli\u003eBestaven, E., Petit, J., Robert, B. \u0026amp; Dehail, P. Center of pressure path during Sit-to-Walk tasks in young and elderly humans. \u003cem\u003eAnnals of Physical and Rehabilitation Medicine\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 644\u0026ndash;651 (2013).\u003c/li\u003e\n\u003cli\u003eChandler, E. A. \u003cem\u003eet al.\u003c/em\u003e Investigating the Relationships Between Three Important Functional Tasks Early After Stroke: Movement Characteristics of Sit-To-Stand, Sit-To-Walk, and Walking. \u003cem\u003eFront Neurol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 660383 (2021).\u003c/li\u003e\n\u003cli\u003eKerr, A., Pomeroy, V. P., Rowe, P. J., Dall, P. \u0026amp; Rafferty, D. Measuring movement fluency during the sit-to-walk task. \u003cem\u003eGait \u0026amp; Posture\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 598\u0026ndash;602 (2013).\u003c/li\u003e\n\u003cli\u003eJones, G. D., James, D. C., Thacker, M. \u0026amp; Green, D. A. Parameters that remain consistent independent of pausing before gait-initiation during normal rise-to-walk behaviour delineated by sit-to-walk and sit-to-stand-and-walk. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e0205346 (2018).\u003c/li\u003e\n\u003cli\u003eDehail, P. \u003cem\u003eet al.\u003c/em\u003e Kinematic and electromyographic analysis of rising from a chair during a \u0026ldquo;Sit-to-Walk\u0026rdquo; task in elderly subjects: Role of strength. \u003cem\u003eClinical Biomechanics\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 1096\u0026ndash;1103 (2007).\u003c/li\u003e\n\u003cli\u003eNorman-Gerum, V. \u0026amp; McPhee, J. Comprehensive description of sit-to-stand motions using force and angle data. \u003cem\u003eJournal of Biomechanics\u003c/em\u003e \u003cstrong\u003e112\u003c/strong\u003e, 110046 (2020).\u003c/li\u003e\n\u003cli\u003eLiang, P. \u003cem\u003eet al.\u003c/em\u003e An Asian-centric human movement database capturing activities of daily living. \u003cem\u003eSci Data\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003eChen, T. \u0026amp; Chou, L.-S. Effects of Muscle Strength and Balance Control on Sit-to-Walk and Turn Durations in the Timed Up and Go Test. \u003cem\u003eArch Phys Med Rehabil\u003c/em\u003e \u003cstrong\u003e98\u003c/strong\u003e, 2471\u0026ndash;2476 (2017).\u003c/li\u003e\n\u003cli\u003eCrenna, F., Rossi, G. B. \u0026amp; Berardengo, M. Filtering Biomechanical Signals in Movement Analysis. \u003cem\u003eSensors (Basel)\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 4580 (2021).\u003c/li\u003e\n\u003cli\u003eWinter, D. Biomechanics and Motor Control of Human Movement, Fourth Edition. (2009) doi:10.1002/9780470549148.ch5.\u003c/li\u003e\n\u003cli\u003eDelp, S. L. \u003cem\u003eet al.\u003c/em\u003e OpenSim: open-source software to create and analyze dynamic simulations of movement. \u003cem\u003eIEEE Trans Biomed Eng\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 1940\u0026ndash;1950 (2007).\u003c/li\u003e\n\u003cli\u003eSeth, A. \u003cem\u003eet al.\u003c/em\u003e OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. \u003cem\u003ePLOS Computational Biology\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, e1006223 (2018).\u003c/li\u003e\n\u003cli\u003eOpenSim. Musculoskeletal Models - OpenSim Documentation. https://simtk-confluence.stanford.edu/display/OpenSim/Musculoskeletal+Models (2021).\u003c/li\u003e\n\u003cli\u003eOpenSim. User\u0026rsquo;s Guide - OpenSim Documentation. https://simtk-confluence.stanford.edu/display/OpenSim/User%27s+Guide (2021).\u003c/li\u003e\n\u003cli\u003eMATLAB-Kmeans. k-means clustering. https://www.mathworks.com/help/stats/kmeans.html (2021).\u003c/li\u003e\n\u003cli\u003eShutaywi, M. \u0026amp; Kachouie, N. N. Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering. \u003cem\u003eEntropy (Basel)\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 759 (2021).\u003c/li\u003e\n\u003cli\u003eThompson, L. A., Badache, M., Cale, S., Behera, L. \u0026amp; Zhang, N. Balance Performance as Observed by Center-of-Pressure Parameter Characteristics in Male Soccer Athletes and Non-Athletes. \u003cem\u003eSports (Basel)\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 86 (2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2718413/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2718413/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSit-to-walk (STW) is categorised by three movement strategies - forward continuation, balance and sit-to-stand-and-walk (SiStW). Literature identified strategies through biomechanical parameters using gold standard laboratory equipment, which is expensive, bulky, and not easily integrated into treatment solutions. As strategy becomes apparent at gait-initiation (GI) and the hip/knee are primary contributors in STW, this study proposes the hip/knee joint angles at GI, as an alternate and standalone method of strategy classification - measurable using wearable sensors. To achieve this, K-means clustering was implemented using three clusters and two feature sets (hip/knee angles); with data from an open access online database (age:21\u0026ndash;80 years; n\u0026thinsp;=\u0026thinsp;10). The results identified forward continuation with the lowest hip/knee extension at GI, followed by balance and then SiStW. From this classification, strategy biomechanics were investigated. The biomechanical parameters (derived in this study) that varied between strategies (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were time, horizontal centre of mass (COM) momentum, braking impulse, centre of pressure (COP) range and velocities, COP-COM separation, hip/knee torque and movement fluency. The derived strategy biomechanics are consistent with literature and validate the classification results. Through strategy classification an individual\u0026rsquo;s strategy-specific biomechanics can be understood and would aid the design and evaluation of interventions for movement impaired individuals.\u003c/p\u003e","manuscriptTitle":"Sit-To-Walk Strategy Classification Using Hip and Knee Joint Angles at Gait Initiation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-31 14:41:19","doi":"10.21203/rs.3.rs-2718413/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-07-20T11:10:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-19T14:16:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-06-08T18:17:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-05-17T12:27:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9092b4b6-6ec5-43da-87dc-3d75b8d44eba","date":"2023-05-14T13:59:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b888fe7e-9ef9-4011-a428-74a497aa027a","date":"2023-05-08T13:09:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-03-30T11:31:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-03-30T11:28:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-03-29T12:01:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-03-29T11:59:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-03-21T12:07:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f932bfa5-5651-462d-abd1-f3681d3c9e52","owner":[],"postedDate":"March 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":20298515,"name":"Physical sciences/Engineering/Biomedical engineering"},{"id":20298516,"name":"Health sciences/Anatomy/Musculoskeletal system"},{"id":20298517,"name":"Health sciences/Health care/Quality of life"}],"tags":[],"updatedAt":"2023-10-09T15:06:40+00:00","versionOfRecord":{"articleIdentity":"rs-2718413","link":"https://doi.org/10.1038/s41598-023-43148-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2023-10-03 15:02:09","publishedOnDateReadable":"October 3rd, 2023"},"versionCreatedAt":"2023-03-31 14:41:19","video":"","vorDoi":"10.1038/s41598-023-43148-0","vorDoiUrl":"https://doi.org/10.1038/s41598-023-43148-0","workflowStages":[]},"version":"v1","identity":"rs-2718413","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2718413","identity":"rs-2718413","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}