Influence of a passive shoulder exoskeleton on drilling performance in women- a cross- sectional study

Influence of a passive shoulder exoskeleton on drilling performance in women- a cross- sectional study | 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 Influence of a passive shoulder exoskeleton on drilling performance in women- a cross- sectional study Julia Katharina Gräf, Bettina Wollesen, Maria Alejandra Diaz, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9019572/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background: Work-related musculoskeletal disorders are common, especially among women performing repetitive overhead tasks. Methods: In a randomized 2x2 crossover study with 14 female participants, we investigated the effects of a passive upper-body exoskeleton during an overhead precision task, involving the tightening of 20 bolts into a sensor-based workstation, while muscle activation, task performance, and usability were assessed. Results: The results showed significant reduced M. Trapezius activations during arm lowering ( p = .041), task duration ( p = .014), and target accuracy ( p < .001) when using the exoskeleton. Subjective strain was significantly lower only in the shoulders ( p = .035). The usability was rated as “unacceptable”, with users criticizing the complexity and learning effort. While the exoskeleton reduced muscle load, its mechanical limitations impaired precision and usability, especially for women. Conclusion: These results highlight the importance of sex-specific, ergonomic, and adaptive designs to improve exoskeleton effectiveness and acceptance. Physical sciences/Engineering Health sciences/Health care Upper-body exoskeleton muscle synergy overhead work user comfort Figures Figure 1 Figure 2 Figure 3 Highlights ● The exoskeleton specifically relieves the shoulder muscles when lowering the arm. ● The exoskeleton supports for larger movement sequences without noticeable restrictions. ● Ergonomic fit, especially for women, is crucial for high-level comfort. 1 Introduction WMSDs are a significant occupational health concern, particularly for tasks requiring repetitive arm and hand movements, such as overhead work (da Costa and Vieira, 2010). According to the European Occupational Health and Safety Report (2019), 58% of employees suffer from WMSDs. The main WMSDs are neck, shoulder, and back pain, resulting in poorer health-related well-being, affecting people worldwide (Govaerts et al., 2021; World Health Organization, 2022). Women have a higher prevalence of WMSDs compared to men (Overstreet et al., 2023), and prevalence also increases with age (European Agency for Safety and Health at Work, 2019; Gill et al., 2023). A specific risk factor for WMSDs is the repetitive lifting of loads in non-ergonomic postures, especially in overhead positions (da Costa and Vieira, 2010). Overhead tasks are highly relevant in different branches, such as manual occupations (Barthelme et al., 2021); however, this overhead work also increases the risk of shoulder and neck pain by 48%. (Barthelme et al., 2021). To address these demands, occupational exoskeletons have emerged as a promising intervention, aiding the arms, shoulders, and torso (Kuber et al., 2022). They can provide mechanical support during activities such as lifting, reaching, and carrying objects, reduce physical strain, promote ergonomic posture and movement, and reduce the level of fatigue (Bances et al., 2020; Bär et al., 2021; de Bock et al., 2023; de Bock et al., 2022; Desbrosses et al., 2021; Garcia et al., 2023; Gillette and Stephenson; Hessinger et al., 2018; Musso et al., 2024; Otten et al., 2018; Pinho and Forner-Cordero, 2022). Reviews have explored exoskeleton effects on task performance and worker perception. Fournier et al. (2023) examined the influence of exoskeleton use on quality and productivity measures (e.g., endurance time, task completion time, number of errors, and number of task cycles completed) and revealed mixed results about the impact of exoskeleton use, dependent on task characteristics. However, it must be noted that within the integrated 15 studies, only five studies examined female participants, and the authors did not provide any information on whether the performance differed between males and females. Similarly, Kuber et al. (2022), Ashta et al. (2023), and Brambilla et al. (2023) indicated that the usage of upper-limb exoskeletons may influence the cognitive workload and physical performance, with outcomes ranging from positive to negative depending on the task and the specific design of the device. For instance, using an exoskeleton may demand additional focus to manage the device, adjust movements, and maintain balance (Bequette et al., 2020), creating an environment that requires a high degree of multitasking (Gräf et al., 2024). Unfortunately, the existing studies on industrial exoskeletons include an 80% male cohort (de Bock et al., 2022), highlighting the lack of knowledge on the impact of the use of exoskeletons on women. Recent efforts addressed this gap, for example, Tyagi et al. (2023) observed that female participants experienced particularly pronounced reductions in shoulder muscle activity compared to male participants. Moreover, Wollesen et al. (2024a; 2024b) emphasized the importance of examining sex[1] differences when using occupational exoskeletons; however, the authors also provided some results that body composition might have more impact on working performance than sex (Wollesen et al., 2024b). Beyond muscle activation, it is essential to examine how exoskeletons influence muscle synergy patterns. Several studies have already shown that the central nervous system can simplify complex movements by grouping co-activated muscles into modular organizational units, called muscle synergies (Bernstein, 1967; Flash and Hochner, 2005; Tresch et al., 2002; Krishnamoorthy et al., 2007). Muscle synergies indicate the relative activation levels of muscles to a synergy, where the absolute activation level is modulated by a single neural command (Ting and McKay, 2007). This means that while each muscle in the synergy maintains a fixed contribution ratio, the overall intensity of activation can increase or decrease depending on the strength of the neural input. So far, the effects of an industrial (upper body) exoskeleton on muscle synergies have only been investigated by Penna et al. (2024). Passive exoskeletons may unexpectedly affect these coordinations between agonist and antagonist muscles, potentially leading to unintended co-contraction or suppressed activation (Mussa-Ivaldi et al., 1994; Theurel and Desbrosses, 2019). Thus, closely monitoring muscle activation patterns and muscle synergies is essential to better understand and optimize exoskeleton use for ergonomic benefits by identifying imbalances such as overuse, suppressed activation, or increased co-contraction, and adjusting assistive force, alignment, or stiffness to support natural coordination and reduce fatigue. However, understanding these effects could be crucial in analysing the complex interaction between the user and the exoskeleton. Furthermore, an inappropriate exoskeleton fit can lead to discomfort and possibly alter body kinematics and increase the risk of injury. McFarland et al. (2022) point out that the extent of these changes is unclear. Moreover, studies show sex-specific differences; while Leibman & Choi (2023) observed no differences between sex for the upper-limb exoskeleton fit and pain during working tasks, Gutierrez et al (2024) noted that women had more barriers to exoskeleton use, assuming that the fitting of an exoskeleton will not capture the anthropometric differences and therefore using an exoskeleton might induce discomfort. Taken together, these findings point out three critical gaps. First, most exoskeleton research has focused on male populations, limiting generalizability to women, who generally have lower muscular strength and different anthropometry compared to men, which increases the risk of MSDs. Sex differences should therefore be taken into consideration, especially when developing and adapting exoskeletons for female users. Second, although reduced muscle activation is well documented, there is a lack of comprehensive analyses of how exoskeletons reshape muscle coordination patterns, as captured by synergy models. Third, performance metrics (e.g., accuracy, speed) and subjective experiences (comfort, usability) have not been systematically compared in female-only cohorts. The present study addresses these gaps by evaluating the effects of an upper-body exoskeleton (Exo4Work) during overhead work in a female cohort. Specifically, we investigate changes in shoulder and arm muscle activity and synergy patterns with and without exoskeleton assistance. Additionally, we investigate task performance, measured in terms of accuracy and duration, and female participants’ perceptions of the exoskeleton comfort and technical experience. We hypothesized that the exoskeleton would reduce shoulder and arm muscle activity compared to unassisted conditions, with potentially larger reductions than what is typically reported in male studies due to the women’s lower baseline strength. This may also indicate an excessive level of support, which could elicit antagonist muscle activation or result in diminished performance. Accordingly, we anticipated a change in task performance through altered accuracy and task duration, and generally positive experiences regarding comfort with the exoskeleton while executing the drilling task. [1] Sex generally refers to a set of biological attributes that are associated with physical and physiological features such as chromosomal genotype, hormonal levels, internal and external anatomy. A binary sex categorization (male/female) is usually designated at birth ("sex assigned at birth") and is in most cases based solely on the visible external anatomy of a newborn. In reality, sex categorizations include people who are intersex/have differences of sex development (DSD). 2 Materials and methods Compliance with ethical standards This study was conducted in accordance with the standards of the Declaration of Helsinki and the local ethical commission (Vrije Universiteit Brussel and Universitair Ziekenhuis Brussel, B.U.N.: 143201941463). All participants provided written informed consent before the study. 2.1 Study design This study followed a 2x2 randomized balanced crossover study design. The participants were randomly assigned to perform the task with and without the exoskeleton, ensuring balanced exposure to each condition. To complete the study protocol, participants attended the laboratory on two separate days, allowing for adequate assessment under exoskeleton and non-exoskeleton conditions. 2.2 Participants Sample size calculation A power analysis using GPower (matched pairs, dz = 0.56, alpha = 0.05, power = 0.8) indicated a required sample size of 22. Accordingly, 21 female participants without prior exoskeleton or industrial task experience could be recruited at or near the VUB campus. Due to poor EMG data (disturbed EMG signals), 7 participants were excluded, resulting in a final sample of 14 women (27 ± 10 years; 163.5 cm ± 3.7 cm; 60.7 kg ± 9.1 kg) for analysis. 2.3 Measurements Surface Electromyography (sEMG) sEMG sensors were placed on the right side of the body, specifically targeting the following muscles: Trapezius (tr), all three heads of the Deltoideus (anterior da, medialis dm, posterior dp), Brachioradialis (br), Biceps brachii (bb), and Triceps brachii (tlh).Before locating the sensors, skin preparation was conducted according to SENIAM guidelines (Hermens et al., 1999; Barbero et al., 2011).To quantify muscle activity, three standardized maximal voluntary isometric contractions (MVC) over 7 seconds were performed for each monitored muscle. The MVC value was calculated as the average peak activity from the two highest-contraction trials. EMG data were collected at a sampling rate of 2000 Hz using the Cometa MiniWave system (Italy). Passive shoulder exoskeleton characteristics In this study, the Exo4Work passive shoulder exoskeleton was used. This exoskeleton is described in De Bock et al (2022). Although the level of assistance provided by the exoskeleton can be adjusted by changing the pretension of the spring, the exoskeleton provides estimated peak assistance of 3 Nm (de Bock et al., 2022). The exoskeleton is worn like a backpack and features a hip belt, shoulder straps, and a chest belt for secure positioning. The upper arms were secured using Velcro straps, similar to commercially available devices. The exoskeleton was individually adjusted for wearer comfort and adds 3.8 kg of weight to the body. RPE and body part discomfort The subjective assessment of the perceived effort was carried out after each trial using the 100-point Borg scale (0-100). 0 corresponds to a very, very low level of effort, while 100 means a very, very high level of effort (Borg, 1998). The rating refers to the overall perceived exertion. Furthermore, for the RPE to specific body regions such as head and neck, shoulders, arms, upper and lower back, buttocks, thighs, knees, lower legs, and feet, chest region, abdominal region, and front of pelvis, the 10-point Borg Scale was used (Borg, 1982). System Usability Scale The System Usability Scale (SUS) is a reliable instrument for measuring user interaction. It consists of ten questions with possible answers ranging from ‘strongly agree’ to ‘strongly disagree’. It provides an overall measure of usability, referring to how easily a user can accomplish their goals when using this device, including user satisfaction and success rates (Bangor et al., 2008; Brooke, 1996). 2.4 Procedures Upon initial arrival, the subject was informed about the protocol and signed an informed consent. All tests took place at the laboratory of the Human Physiology and Sports Physiotherapy Research Group (MFYS, VUB). The experiment consisted of a total of three laboratory visits in which general participant characteristics (e.g., body height and weight, measurements of body parts) were initially recorded (visit 1) and participants were introduced to the experimental protocol, the laboratory environment, and the Exo4Work exoskeleton (familiarization of around 1 hour). Over the following two visits, the participants completed the experimental protocol. Each trial lasted approximately 1.5 hours. In between the first and the second visit, at least 48 hours were scheduled. In between the second and the third laboratory visit, 6 to 9 days were foreseen. 2.4.1 Familiarization The first visit to the lab included familiarization with the study protocol and the exoskeleton to get to know the routine and to reduce learning effects throughout the experimental trials. This involved the execution of the overhead precision task with the Exo4Work exoskeleton (Rossini et al., 2021) up to 12 times with a 3-minute break in between each trial. 2.4.2 Experimental protocol The experimental trials included the execution of a custom precision task under exoskeleton and non-exoskeleton conditions, following the protocol by De Bock et al. (2022), and based on methods developed by Kim et al. (2018) to evaluate overhead work precision. After locating the sEMG sensors and performing the 3 MVC tests, the Exo4Work was applied and adjusted to the participant with the assistance of the investigators. After each trial, the RPE and a local body part discomfort scale were filled out. Additionally, the SUS was filled out at the end of the session with the exoskeleton. 2.4.3 Overhead precision task Participants utilized a Black & Decker electric screwdriver (1.14 kg) to tighten 20 bolts pre-inserted into an aluminium plate positioned overhead (cf. Figure 1).Force sensors and accelerometers were integrated into the overhead working setup to quantify working performance (duration and accuracy) and to facilitate the segmentation of acquired signals.Participants pressed a push button at pelvic crest height, tightened a bolt at overhead height, and pressed the button again to indicate movement initiation (as indicated in Figure 1). Sensorized aluminium and plexiglass plates allowed tracking contact between the screwdriver bit and the bolt, as well as monitoring screwing errors. The appropriate overhead height was determined using the method described by Sood et al. (2007), which calculates hand height with the shoulder and elbow at a 90-degree angle, plus 0.4 times the difference between hand height with the arm fully extended and hand height at the 90-degree angle. 2.5 Analysis EMG The For consistency, the second trial was used, as the first and last trials often showed poor-quality data. The raw EMG signals were bandpass filtered (Butterworth, 4th order, 20–500 Hz), rectified, and smoothed using a 100ms root mean square (RMS). Subsequently, the EMG signal quality was visually inspected, and 7 participants were excluded due to excessive noise or artifacts. Signals were then normalized to the MVC values. Movement cycles (from lifting the arm, drilling, lowering the arm) were segmented, excluding irrelevant movements, and averaged per participant using a 200-point interpolation. Muscle Synergies To identify the muscle synergies, a non-negative matrix factorization was performed for each participant using three synergies with an imposed factorization rank of three, determined based on the variance accounted for as the criterion for identifying the optimal number of synergies with a range of around 90%. This analysis results in weighted muscle vectors (W) that define the relative contribution of each muscle to a synergy, while H refers to the activation coefficients that capture the time-varying activation level of each synergy across tasks or conditions. Finally, muscle synergies were clustered based on their activation patterns (H) into three distinct clusters via k-means clustering (MATLAB k-means++ algorithm) (Raj & Palaniappan, 2024). Therefore, peak locations and full-width half max were calculated. Reported will also be W, representing the clustering towards the minimal variance in H. The resulting muscle weights were then used for statistical calculations, and the activation patterns were used to describe the movement phases. The data was organized into three clusters, each corresponding to a different phase of the movement. Performance Performance data, including task, aiming, and drilling duration as well as error integral during aiming and error integral during drilling, were analysed and reported as mean ± standard deviation (SD), with statistical comparisons conducted using repeated measures ANOVA to assess within-subject differences between Exo and noExo conditions. RPE and SUS The Borg Scale RPE data was analyzed and reported as mean ± SD to summarize central tendency and variability for overall, as well as the specific body regions (cf. 2.3). SUS scores were calculated according to Brooke (1996) and Bangor et al. (2008), yielding values ranging from 0 to 100, with 100 representing the best imaginable and highly acceptable usability. Scores below 50 indicate unacceptable usability, and scores between 50-70 are considered marginally acceptable. Statistical Procedures For all statistical analyses, normality of distribution was tested using Kolmogorov-Smirnov. The corresponding comparative test was then selected. The Wilcoxon-signed rank test was employed for the muscle synergy data using W, as the data is clustered towards minimal variance in H, and the RPE data, while repeated measures ANOVA (with Exo and no Exo as within-subject factors) was utilized for the performance data. All analyses were performed using MATLAB 2019. a, Python 3.6, Microsoft Excel 2016, and SPSS 29.0, with alpha set at 0.05. 3 Results 3.1 Muscle Synergy Figure 2 illustrates the mean activation patterns (H) and the weighted muscle (W) of the seven muscles with (E2) and without (E1) the exoskeleton. Figure 2 shows muscle synergy cluster analysis with and without exoskeleton across three task phases: Cluster 1 (arm elevation), Cluster 2 (drilling), and Cluster 3 (arm lowering). Each cluster presents bar charts of weighted muscle activations for seven muscles and line plots of corresponding activation patterns. Without the exoskeleton, some muscles show higher activation in specific phases, while with the exoskeleton, activations appear more evenly distributed across muscles No Exo (E1) In Cluster 1 (arm elevation), the muscles tr and da showed relatively higher mean weights compared to the other muscles, indicating their prominent roles during arm elevation. The other muscles exhibit lower activation levels, though they still contribute to the movement. In Cluster 2 (drilling phase), muscle weights across all muscles appeared more balanced compared to Cluster 1, suggesting that stabilization is well distributed. In Cluster 3 (arm returning), the muscle weights of all seven muscles remained relatively consistent, except for the br with higher activation compared to the other involved muscles. Overall, the muscles tlh and dm consistently showed higher levels of activation across clusters compared to other muscles (cf. Table 1). Exo (E2) In Cluster 1 (arm elevation phase), the muscle weights were relatively consistent across all muscles and contributed moderately, indicating a well-distributed muscle engagement. Similar to Cluster 1, the mean weights across all muscles in Cluster 2 (drilling phase) remained evenly distributed, suggesting that the stabilization and effort required during the drilling task were shared among all muscles. In Cluster 3 (arm returning phase), the weighted muscles were also balanced, similar to Clusters 1 and 2, with consistent and controlled muscle engagement. Furthermore, the comparison of the muscles between the Exo and noExo conditions revealed that the muscle weights slightly differ between both conditions. A significant difference was observed for the tr during Cluster 3 (arm returning phase), with lower activation in the Exo condition compared to noExo condition ( Z = -2.040 and p = 0.041) (cf. Table 1). This suggests that the exoskeleton reduced the reliance on the M. Trapezius for scapular stabilization and control during the downward movement of the arm. For other muscles and clusters, although the trends in muscle weights differed qualitatively between both conditions, no statistically significant differences were found (cf. Table 1). Table 1. Weighted muscle data. Cluster 1 muscle no Exo [M ± SD] Exo [M ± SD] Z p bb 0.559 ± 0.338 0.484 ± 0.253 -0.534 0.594 br 0.298 ± 0.394 0.376 ± 0.387 -0.549 0.583 da 0.458 ± 0.398 0.534 ± 0.383 -0.345 0.730 dm 0.548 ± 0.301 0.614 ± 0.336 -0.471 0.638 dp 0.338 ± 0.387 0.492 ± 0.340 -1.083 0.279 tlh 0.456 ± 0.292 0.435 ± 0.348 -0.031 0.975 tr 0.532 ± 0.336 0.432 ± 0.355 -1.223 0.221 Cluster 2 bb 0.236 ± 0.250 0.353 ± 0.341 -0.734 0.463 br 0.257 ± 0.351 0.447 ± 0.393 -1.293 0.196 da 0.399 ± 0.421 0.270 ± 0.249 -0.454 0.650 dm 0.461 ± 0.330 0.217 ± 0.244 -1.852 0.064 dp 0.530 ± 0.336 0.306 ± 0.336 -1.852 0.064 tlh 0.438 ± 0.290 0.496 ± 0.365 -0.596 0.511 tr 0.384 ± 0.319 0.443 ± 0.327 -0.785 0.433 Cluster 3 bb 0.320 ± 0.325 0.454 ± 0.327 -0.973 0.331 br 0.686 ± 0.323 0.408 ± 0.344 -1.853 0.064 da 0.310 ± 0.317 0.442 ± 0.279 -0.910 0.363 dm 0.240 ± 0.266 0.399 ± 0.266 -1.569 0.177 dp 0.299 ± 0.315 0.403 ± 0.408 -0.534 0.594 tlh 0.276 ± 0.329 0.335 ± 0.347 -0.596 0.511 tr 0.246 ± 0.214 0.425 ± 0.329 -2.040 0.041 Note: Anterior Deltoid (da), Medial Deltoid (dm), Posterior Deltoid (dp), Biceps Brachii (bb), Long Head of Triceps Brachii (tlh), Brachioradialis (br), Trapezius Descendens (tr), M = mean, SD = standard deviation, Z = Wilcoxon signed-rank test 3.2 Performance The overall task duration did not differ significantly between conditions, with an average of 2.18 ± 0.25 seconds in the noExo condition and 2.24 ± 0.24 seconds in the Exo condition (F = 1.231, p = .287, η² = .086). However, the aiming phase lasted significantly longer when using the exoskeleton (1.46 ± 0.15 sec) compared to the noExo condition (1.40 ± 0.13 sec), with a moderate effect size ( F = 2.504, p = .0138, η² = .161). In contrast, the duration of the drilling phase remained nearly identical between conditions (0.78 ± 0.18 sec vs. 0.782 ± 0.22 sec; F = .001, p = .974, η² = .000). Regarding accuracy, a notable difference was found in the mean error integral during aiming, which was significantly higher in the Exo condition (0.2 ± 0.0) compared to noExo (0.1 ± 0.0) ( F = 73.393, p < .001; η² = .850). This indicates a strong impact of the exoskeleton on aiming precision. In contrast, the mean error integral during drilling showed no significant difference between conditions ( F = .636, p = .439, η² = .047) (cf. Table 2). Table 2. Performance data. Performance noExo [M ± SD] Exo [M ± SD] Differences [ F, p, η² ] task duration (sec) 2180.6 ± 251.7 2236.0 ± 237.1 1.231, .287, .086 duration aiming 1403.0 ± 131.3 1459.7 ± 148.7 2.504, 0.138, .161 duration drilling 777.6 ± 180.4 776.2 ± 222.5 .001, .974, .000 error integral aiming 0.1 ± 0.0 0.2 ± 0.0 73.393, <.001, .850 error integral drilling 0.1 ± 0.0 0.1 ± 0.1 .636, .439, .047 Note: M = mean, SD = standard deviation, F = repeated measures ANOVA 3.3 RPE Table 3 presents the results of RPE on the Borg 100-point scale, comparing two conditions: noExo (without an exoskeleton) and Exo (with an exoskeleton), as well as the specific RPE for different body regions on the Borg 10-point scale. The overall RPE was slightly lower in the Exo condition (32 ± 13) compared to noExo (37 ± 16), but this difference was not statistically significant ( Z = -1.384, p = .166). Similarly, most body regions showed no significant differences in RPE between conditions. However, a notable exception was found for the shoulders, where RPE was significantly lower in the Exo condition ( Z = -2.111, p = .035). Table 3. RPE results. noExo [M ± SD] Exo [M ± SD] Differences[ Z, p] RPE overall (0-100) 37 ± 16 32 ± 13 -1.384, .166 Head and neck 2 ± 1 2 ± 1 -1.823, .068 Shoulders 3 ± 1 3 ± 1 -2.111, .035 Arms 3 ± 1 3 ± 1 -1.155, .248 Upper back 2 ±1 2 ± 1 -.378, .705 Lower back 2 ± 1 2 ± 1 -1.000, .317 Buttocks 1 ± 1 1 ± 1 -1.732, .083 Tights 1 ± 1 1 ± 1 -1.414, .157 Knees 1 ± 1 1 ± 1 .000, 1.000 Lower legs and feet 1 ± 1 1 ± 1 .000, 1.000 Chest region 1 ± 1 2 ± 1 .000, 1.000 Abdominal region 1 ± 1 2 ± 1 -.378, .705 Front of pelvis 1 ± 1 1 ± 1 -1.000, .317 Note: M = mean, SD = standard deviation, Z = Wilcoxon signed-rank test 3.4 SUS The results of the SUS for the evaluation of the exoskeleton showed total scores ranging from 7.5 to 67.5 (28.4 ± 14.8), indicating usability levels from unacceptable to marginally acceptable. The results indicate that users found the system challenging to learn, as the statement ‘I needed to learn a lot of things before I could get going with this system’ received a high level of agreement. Similarly, many users found the system cumbersome and unnecessarily complex, suggesting difficulties in navigation and ease of use. Confidence in using the system was relatively low, as indicated by the lower scores for ‘I felt very confident using the system’ and ‘I thought the system was easy to use’. Additionally, users did not strongly agree that they would like to use the system frequently, implying dissatisfaction with its usability. While the integration of various functions received a neutral rating, the responses suggest inconsistencies within the system, as indicated by the agreement with ‘I thought there was too much inconsistency in this system’ (cf. Figure 3). Figure 3 shows the SUS results as bar chart with mean ratings and error bars across ten SUS items. Responses range from strongly disagree (1) to strongly agree (5). Participants reported high agreement with needing to learn a lot before use, finding the system cumbersome, inconsistent, and unnecessarily complex. They disagreed with statements that the system was easy to use, quick to learn, or well-integrated. Confidence in using the system and willingness to use it frequently scored low to moderate. 4 Discussion Physically demanding tasks involving the upper limbs continue to challenge workplace ergonomics and promote interest in assistive technologies, such as exoskeletons. Given that such tasks increase the risk of arm pain by 18% (Barthelme et al., 2021), exoskeletons have been explored as potential solutions for reducing muscular strain and improving work ergonomics (Desbrosses et al., 2021; Hessinger et al., 2018; Kuber et al., 2022; Musso et al., 2024), while effects on muscle synergy patterns, task performance and usability remain debated (Fournier et al., 2023). The present study offers new insights by evaluating these aspects in female participants, revealing a complex interplay between muscle relief, movement constraints, and user adaptation. A particular strength of this study is its exclusive focus on female users, an underrepresented population in exoskeleton research, which traditionally centers on male subjects (De Bock et al., 2022; Fournier et al., 2023). By filling this gap, our study contributes valuable sex-specific insights into how a passive upper-body exoskeleton, specifically designed for male users, affects neuromuscular coordination, task performance and usability. Our findings not only highlight the potential challenges in usability and performance for female users, but also emphasize the need for more inclusive design approaches in the future development of exoskeletons. Consistent with previous findings (Gillette and Stephensond, 2018; Hessinger et al., 2018; Otten et al., 2018), our results show a significant reduction in muscle activation in the M. Trapezius during arm descent (cluster 3) when using the exoskeleton. This confirms that the passive exoskeleton effectively reduced the reliance on the M. Trapezius for scapular stabilization, as expected (van Engelhoven et al., 2018; Wu et al., 2018). It is important to note that the exoskeleton used in this study is a passive device, designed primarily to support arm elevation and hold the arm lifted, rather than dynamically assisting movements across all phases. Interestingly, despite this intended function, no substantial differences were found in Cluster 1, where the movement involves arm lifting. Instead, the effects were more pronounced in clusters 2 (drilling phase) and 3 (arm lowering phase), where muscle stabilization plays a greater role. Furthermore, our study did not show a uniform reduction in muscle activation, but rather a redistribution of effort. In cluster 2, the balanced activation across muscles suggests a shared stabilizing effort, potentially shaped by the mechanical support of the exoskeleton. This may reflect compensatory strategies, as the exoskeleton’s constraints could alter natural joint dynamics (Theurel and Desbrosses, 2019). The relatively similar mean activations across all muscles support this interpretation and suggest that stabilizing was distributed among them rather than dominated by a single muscle. This balanced pattern might also be the result of individual differences in dominant muscle activation strategies, which, when averaged, appear more uniform. Notably, Tyagi et al. (2023) found that female participants exhibited significant reductions in shoulder muscle activity when using exoskeletons, especially in the deltoideus muscle. Given that women typically have lower absolute muscular strength and different anthropometric proportions than men (McFarland et al., 2022), the fixed support level was excessive for some participants, potentially causing unintended compensatory muscle activation. A kinematic analysis of arm trajectories would help clarify how these mechanical constraints influenced motor control and precision. Despite muscular benefits, the exoskeleton did not improve task efficiency. Contrary to expectations, aiming duration increased, and aiming precision significantly declined. This aligns with Fournier et al. (2023), who found that exoskeleton effects on productivity vary depending on task demands. One possible explanation is that the exoskeleton restricted natural joint dynamics, requiring participants to adjust their coordination strategies. Research indicates that these mechanical constraints can interfere with fine motor skills and induce postural discomfort (Luger et al., 2023), especially in tasks requiring high precision. Additionally, cognitive load may increase when participants must compensate for restricted movement (De Bock et al., 2022). Thus, while the exoskeleton likely facilitated large motor effort, its rigidity may have impaired the subtle, corrective movements needed for accuracy. Furthermore, adaptation time is crucial in exoskeleton performance outcomes (De Bock et al., 2022). Although our participants underwent a familiarization period, they were not experienced exoskeleton users. Therefore, the observed decline in aiming accuracy may still reflect an early stage learning curve rather than a fundamental limitation of the device. With extended training, performance might improve over time. Against this background, usability ratings provide a key explanation for the observed trade-offs between muscle relief and task performance. The low SUS score indicates that participants found the exoskeleton prototype difficult to use. This aligns with Gutierrez et al. (2024), who reported that women often face challenges in exoskeleton adaptation due to suboptimal anthropometric fit, especially in early-stage prototypes. Although some studies (Leibman and Choi, 2023) report no significant sex differences in upper-limb exoskeleton fit, our participants reported discomfort and adjustment difficulties, suggesting that the ergonomic design may still need optimization for female users. This discomfort may explain why overall RPE did not decrease significantly, despite a localized reduction in muscle shoulder activity. As Arauz et al. (2024) emphasized, users often value muscle relief, but poor usability negates these benefits. Participants might have compensated for mechanical constraints by increasing stabilizing effort, thereby counteracting the expected fatigue relief. These findings are consistent with Elprama et al. (2022), who identified comfort, ease of use, and ergonomic fit as key factors for exoskeleton acceptance. Discomfort related to weight, thermal comfort, or fit can limit perceived usefulness and reduce long-term willingness to use the exoskeleton. In our study, negative experiences may reflect low confidence in the exoskeleton’s effectiveness and a lack of task compatibility. Ultimately, while localized muscle relief was achieved, the usability issues overshadowed these benefits, contributing to the overall lack of perceived improvements in RPE. Given the rising incidence of WMSDs (Govaerts et al., 2021; Overstreet et al., 2023), it is crucial to optimize exoskeletons for both ergonomic support and functional usability. Future research should focus on developing task-specific adaptations, sex-inclusive designs, and understanding long-term adaptation effects to maximize the practical benefits of exoskeletons in industrial and occupational contexts. While this study offers valuable insights into female-specific responses to passive upper-limb exoskeleton use, several limitations should be considered. First, participants had limited prior experience with exoskeletons, and the brief familiarization period may have been insufficient for full adaptation. It is possible that the observed reductions in accuracy and perceived usability were influenced by this early stage learning curve. Future studies should incorporate longer training protocols to assess whether motor coordination, user comfort, and performance improve over time. Second, this study examined only a single passive shoulder-support exoskeleton with a fixed level of assistance. While this allows for controlled analysis, it also limits the applicability of the findings to other exoskeleton types. Lastly, the insufficient sample size about the a priori power analysis is an important limitation of this study. This lack of 8 participants compared to the power analysis reduces the statistical significance of the study and consequently limits the informative value and generalizability of the conclusions to be drawn from the data. In conclusion, our findings highlight the importance of designing exoskeletons that consider sex-specific differences in strength, anthropometry, and movement patterns. While exoskeletons can alleviate muscle strain, they need to support natural movement to avoid unintended compensations. Although the exoskeleton in our study reduced shoulder muscle activity, it negatively impacted aiming accuracy and increased task duration, likely due to mechanical limitations and usability challenges. This underscores the complex relationship between biomechanical relief, motor control, and user experience during exoskeleton-assisted tasks. Future research should systematically investigate sex-specific differences and adapt exoskeletons more closely to different user groups. Studies with comprehensive evaluation approaches are necessary to better understand the balance between biomechanical relief, precision of movement, and user acceptance and to optimize usability in real working environments. Declarations Ethics approval and consent to participate The study was approved by the local ethics committee as an amendment at the Vrije Universiteit Brussel. All participants signed a written consent for participation. Consent for publication All authors have read and approved the final manuscript and consent to its publication. Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no conflicts of interest. Funding This work was supported by the Brussels Institute of Advanced Studies (Grant: BrIAS2024). Maria Alejandra Diaz was funded by the Federal Public Service for Policy and Support (AidWear project). Contributions JKG: Formal analysis, visualization, writing - original draft; BW: Conceptualization, Methodology, Supervision, Writing – review & editing; MD: Data collection, Formal analysis, Methodology, Writing – review & editing; SDB: Conceptualization, Writing – review & editing; LH: Formal analysis, Writing – review & editing; VD: Technical support; AP: Data collection, Methodology; BR: Writing – review & editing. Acknowledgement Not applicable References Arauz, P.G., Chavez, G., Reinoso, V., Ruiz, P., Ortiz, E., Cevallos, C., Garcia, G., 2024. 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Wearable Technologies 5, e14. Pinho, J.P., Forner-Cordero, A., 2022. Shoulder muscle activity and perceived comfort of industry workers using a commercial upper limb exoskeleton for simulated tasks. Applied ergonomics 101, 103718. Raj, Edward Jero Sam Jeeva, Palaniappan, R., 2024. A novel method for movement quality analysis of lower limb joints using surface electromyography signals and k-means clustering technique. Biomedical Signal Processing and Control 95, 106455. Rossini, M., Bock, S. de, van der Have, A., Flynn, L., Rodriguez-Cianca, D., Pauw, K. de, Lefeber, D., Geeroms, J., Rodriguez-Guerrero, C., 2021. Design and evaluation of a passive cable-driven occupational shoulder exoskeleton. IEEE Transactions on Medical Robotics and Bionics 3, 1020–1031. SENIAM. http://seniam.org/sensor_location.htm (accessed 23 November 2021). Sood, D., Nussbaum, M.A., Hager, K., 2007. Fatigue during prolonged intermittent overhead work: reliability of measures and effects of working height. Ergonomics 50, 497–513. Theurel, J., Desbrosses, K., 2019. Occupational exoskeletons: overview of their benefits and limitations in preventing work-related musculoskeletal disorders. IISE Transactions on Occupational Ergonomics and Human Factors 7, 264–280. Ting, L.H., McKay, J.L., 2007. Neuromechanics of muscle synergies for posture and movement. Current opinion in neurobiology 17, 622–628. Tyagi, O., Mukherjee, T.R., Mehta, R.K., 2023. Neurophysiological, muscular, and perceptual adaptations of exoskeleton use over days during overhead work with competing cognitive demands. Applied ergonomics 113, 104097. van Engelhoven, L., Poon, N., Kazerooni, H., Barr, A., Rempel, D., Harris-Adamson, C., 2018 Evaluation of an adjustable support shoulder exoskeleton on static and dynamic overhead tasks, in: Proceedings of the Human Factors and Ergonomics Society Annual Meeting. SAGE Publications Sage CA: Los Angeles, CA, pp. 804–808. Wollesen, B., Gräf, J., Bock, S. de, Alfio, E., Díaz, M.A., Pauw, K. de, 2024a. Gender Differences in Performing an Overhead Drilling Task Using an Exoskeleton—A Cross-Sectional Study. Biomimetics 9, 601. Wollesen, B., Gräf, J., Hansen, L., Gurevich, A., Elprama, S.A., Argubi-Wollesen, A., Pauw, K. de, 2024b. Gender differences in the use of an upper-extremity exoskeleton during physically and cognitively demanding tasks-a study protocol for a randomized experimental trial. Frontiers in Neurology 15, 1401937. Wu, Wen; Fong, Justin; Crocher, Vincent; Lee, Peter V. S.; Oetomo, Denny; Tan, Ying; Ackland, David C. (2018): Modulation of shoulder muscle and joint function using a powered upper-limb exoskeleton. In: Journal of biomechanics 72, S. 7–16. 2019. Work-related musculoskeletal disorders: prevalence, costs and demographics in the EU-European risk observatory report. Additional Declarations No competing interests reported. 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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-9019572","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":604644749,"identity":"bdc399dd-8812-4b56-93c7-b1a59f140141","order_by":0,"name":"Julia Katharina Gräf","email":"data:image/png;base64,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","orcid":"","institution":"Universität Hamburg","correspondingAuthor":true,"prefix":"","firstName":"Julia","middleName":"Katharina","lastName":"Gräf","suffix":""},{"id":604644750,"identity":"bff87fb0-c8e1-497a-b9f9-1bc1e978a0d6","order_by":1,"name":"Bettina Wollesen","email":"","orcid":"","institution":"German Sport University Cologne","correspondingAuthor":false,"prefix":"","firstName":"Bettina","middleName":"","lastName":"Wollesen","suffix":""},{"id":604644751,"identity":"b4a83c7d-f77f-41c2-9c34-df867bc6a182","order_by":2,"name":"Maria Alejandra Diaz","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Alejandra","lastName":"Diaz","suffix":""},{"id":604644753,"identity":"99085dfd-355b-4fc6-a8a3-6ae1252a3b67","order_by":3,"name":"Sander De Bock","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Sander","middleName":"","lastName":"De Bock","suffix":""},{"id":604644754,"identity":"a9612413-996c-44ae-bf93-654b4cc13e32","order_by":4,"name":"Lasse Hansen","email":"","orcid":"","institution":"German Sport University Cologne","correspondingAuthor":false,"prefix":"","firstName":"Lasse","middleName":"","lastName":"Hansen","suffix":""},{"id":604644755,"identity":"832cf200-e4ac-4789-9003-60da0969ed19","order_by":5,"name":"Vincent Ducastel","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Ducastel","suffix":""},{"id":604644756,"identity":"25469b6c-8313-41d3-9729-6e060f0920ef","order_by":6,"name":"Alessandra Preckher","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Alessandra","middleName":"","lastName":"Preckher","suffix":""},{"id":604644757,"identity":"70d2f9d4-240c-4cba-b0dc-a2e1d8aa599e","order_by":7,"name":"Bart Roelands","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Bart","middleName":"","lastName":"Roelands","suffix":""},{"id":604644758,"identity":"4617c982-286e-4723-9642-603f9c865b78","order_by":8,"name":"Kevin De Pauw","email":"","orcid":"","institution":"Vrije Universiteit Brussel","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"De Pauw","suffix":""}],"badges":[],"createdAt":"2026-03-03 11:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9019572/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9019572/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104572167,"identity":"6b5c7c82-9efa-4cde-a371-588bed457e62","added_by":"auto","created_at":"2026-03-13 12:59:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69570,"visible":true,"origin":"","legend":"\u003cp\u003eDrilling task performance.\u003c/p\u003e\n\u003cp\u003eFigure 1 shows the overhead precision task setup: participants used a 1.14 kg electric screwdriver to tighten 20 bolts in an overhead aluminium plate, with sensors tracking performance, movement, and errors.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9019572/v1/6031a0dfcfaae720b40a32f9.png"},{"id":104572169,"identity":"8989d8d1-9dff-4f07-891e-a9f21cdd0818","added_by":"auto","created_at":"2026-03-13 12:59:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":264848,"visible":true,"origin":"","legend":"\u003cp\u003eMuscle synergy cluster analysis. \u003cem\u003eE1 = NoExo, E2 = Exo.; W= the time-invariant muscle synergy vectors; H=time-dependent activation coefficients; Anterior Deltoid (da), Medial Deltoid (dm), Posterior Deltoid (dp), Biceps Brachii (bb), Long Head of Triceps Brachii (tlh), Brachioradialis (br), Trapezius Descendens (tr)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9019572/v1/edd0bdc998b36ef6beb491e5.png"},{"id":104808352,"identity":"c813d5f3-c7cd-462b-9946-85d07b9972e3","added_by":"auto","created_at":"2026-03-17 12:36:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124478,"visible":true,"origin":"","legend":"\u003cp\u003eSUS results for Exoskeleton use.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9019572/v1/5e5e59f4009d5f8bdd8e9468.png"},{"id":104809507,"identity":"03a8a612-5655-495b-8ebf-321f2ad19582","added_by":"auto","created_at":"2026-03-17 12:51:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1297733,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9019572/v1/be2aba40-fba9-4e6e-a63a-ad80c0b4c686.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of a passive shoulder exoskeleton on drilling performance in women- a cross- sectional study","fulltext":[{"header":"Highlights","content":"\u003cp\u003e● The exoskeleton specifically relieves the shoulder muscles when lowering the arm.\u003c/p\u003e\u003cp\u003e● The exoskeleton supports for larger movement sequences without noticeable restrictions.\u003c/p\u003e\u003cp\u003e● Ergonomic fit, especially for women, is crucial for high-level comfort.\u003c/p\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eWMSDs are a significant occupational health concern, particularly for tasks requiring repetitive arm and hand movements, such as overhead work (da Costa and Vieira, 2010). According to the European Occupational Health and Safety Report (2019), 58% of employees suffer from WMSDs. The main WMSDs are neck, shoulder, and back pain, resulting in poorer health-related well-being, affecting people worldwide (Govaerts et al., 2021; World Health Organization, 2022). Women have a higher prevalence of WMSDs compared to men (Overstreet et al., 2023), and prevalence also increases with age (European Agency for Safety and Health at Work, 2019; Gill et al., 2023). \u0026nbsp;A specific risk factor for WMSDs is the repetitive lifting of loads in non-ergonomic postures, especially in overhead positions (da Costa and Vieira, 2010). Overhead tasks are highly relevant in different branches, such as manual occupations (Barthelme et al., 2021); however, this overhead work also increases the risk of shoulder and neck pain by 48%. (Barthelme et al., 2021). To address these demands, occupational exoskeletons have emerged as a promising intervention, aiding the arms, shoulders, and torso (Kuber et al., 2022). They can provide mechanical support during activities such as lifting, reaching, and carrying objects, reduce physical strain, promote ergonomic posture and movement, and reduce the level of fatigue (Bances et al., 2020; B\u0026auml;r et al., 2021; de Bock et al., 2023; de Bock et al., 2022; Desbrosses et al., 2021; Garcia et al., 2023; Gillette and Stephenson; Hessinger et al., 2018; Musso et al., 2024; Otten et al., 2018; Pinho and Forner-Cordero, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReviews have explored exoskeleton effects on task performance and worker perception. Fournier et al. (2023) examined the influence of exoskeleton use on quality and productivity measures (e.g., endurance time, task completion time, number of errors, and number of task cycles completed) and revealed mixed results about the impact of exoskeleton use, dependent on task characteristics. However, it must be noted that within the integrated 15 studies, only five studies examined female participants, and the authors did not provide any information on whether the performance differed between males and females. Similarly, Kuber et al. (2022), Ashta et al. (2023), and Brambilla et al. (2023) indicated that the usage of upper-limb exoskeletons may influence the cognitive workload and physical performance, with outcomes ranging from positive to negative depending on the task and the specific design of the device. For instance, using an exoskeleton may demand additional focus to manage the device, adjust movements, and maintain balance (Bequette et al., 2020), creating an environment that requires a high degree of multitasking (Gr\u0026auml;f et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnfortunately, the existing studies on industrial exoskeletons include an 80% male cohort (de Bock et al., 2022), highlighting the lack of knowledge on the impact of the use of exoskeletons on women. Recent efforts addressed this gap, for example, Tyagi et al. (2023) observed that female participants experienced particularly pronounced reductions in shoulder muscle activity compared to male participants. Moreover, Wollesen et al. (2024a; 2024b) emphasized the importance of examining sex[1] differences when using occupational exoskeletons; however, the authors also provided some results that body composition might have more impact on working performance than sex (Wollesen et al., 2024b).\u003c/p\u003e\n\u003cp\u003eBeyond muscle activation, it is essential to examine how exoskeletons influence muscle synergy patterns. Several studies have already shown that the central nervous system can simplify complex movements by grouping co-activated muscles into modular organizational units, called muscle synergies (Bernstein, 1967; Flash and Hochner, 2005; Tresch et al., 2002; Krishnamoorthy et al., 2007). Muscle synergies indicate the relative activation levels of muscles to a synergy, where the absolute activation level is modulated by a single neural command (Ting and McKay, 2007). This means that while each muscle in the synergy maintains a fixed contribution ratio, the overall intensity of activation can increase or decrease depending on the strength of the neural input. So far, the effects of an industrial (upper body) exoskeleton on muscle synergies have only been investigated by Penna et al. (2024). Passive exoskeletons may unexpectedly affect these coordinations between agonist and antagonist muscles, potentially leading to unintended co-contraction or suppressed activation (Mussa-Ivaldi et al., 1994; Theurel and Desbrosses, 2019). Thus, closely monitoring muscle activation patterns and muscle synergies is essential to better understand and optimize exoskeleton use for ergonomic benefits by identifying imbalances such as overuse, suppressed activation, or increased co-contraction, and adjusting assistive force, alignment, or stiffness to support natural coordination and reduce fatigue. However, understanding these effects could be crucial in analysing the complex interaction between the user and the exoskeleton.\u003c/p\u003e\n\u003cp\u003eFurthermore, an inappropriate exoskeleton fit can lead to discomfort and possibly alter body kinematics and increase the risk of injury.\u0026nbsp;McFarland et al. (2022) point out that the extent of these changes is unclear. Moreover, studies show sex-specific differences; while Leibman \u0026amp; Choi (2023) observed no differences between sex for the upper-limb exoskeleton fit and pain during working tasks, Gutierrez et al (2024) noted that women had more barriers to exoskeleton use, assuming that the fitting of an exoskeleton will not capture the anthropometric differences and therefore using an exoskeleton might induce discomfort.\u003c/p\u003e\n\u003cp\u003eTaken together, these findings point out three critical gaps. First, most exoskeleton research has focused on male populations, limiting generalizability to women, who generally have lower muscular strength and different anthropometry compared to men, which increases the risk of MSDs. Sex differences should therefore be taken into consideration, especially when developing and adapting exoskeletons for female users.\u0026nbsp; \u0026nbsp;Second, although reduced muscle activation is well documented, there is a lack of comprehensive analyses of how exoskeletons reshape muscle coordination patterns, as captured by synergy models. Third, performance metrics (e.g., accuracy, speed) and subjective experiences (comfort, usability) have not been systematically compared in female-only cohorts.\u003c/p\u003e\n\u003cp\u003eThe present study addresses these gaps by evaluating the effects of an upper-body exoskeleton (Exo4Work) during overhead work in a female cohort. Specifically, we investigate changes in shoulder and arm muscle activity and synergy patterns with and without exoskeleton assistance. Additionally, we investigate task performance, measured in terms of accuracy and duration, and female participants\u0026rsquo; perceptions of the exoskeleton comfort and technical experience.\u003c/p\u003e\n\u003cp\u003eWe hypothesized that the exoskeleton would reduce shoulder and arm muscle activity compared to unassisted conditions, with potentially larger reductions than what is typically reported in male studies due to the women\u0026rsquo;s lower baseline strength. This may also indicate an excessive level of support, which could elicit antagonist muscle activation or result in diminished performance. Accordingly, we anticipated a change in task performance through altered accuracy and task duration, and generally positive experiences regarding comfort with the exoskeleton while executing the drilling task.\u003c/p\u003e\n\u003cp\u003e[1] Sex generally refers to a set of biological attributes that are associated with physical and physiological features such as chromosomal genotype, hormonal levels, internal and external anatomy. A binary sex categorization (male/female) is usually designated at birth (\u0026quot;sex assigned at birth\u0026quot;) and is in most cases based solely on the visible external anatomy of a newborn. In reality, sex categorizations include people who are intersex/have differences of sex development (DSD).\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompliance with ethical standards\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the standards of the Declaration of Helsinki and the local ethical commission (Vrije Universiteit Brussel and Universitair Ziekenhuis Brussel, B.U.N.: 143201941463). All participants provided written informed consent before the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1 Study design\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study followed a 2x2 randomized balanced crossover study design. The participants were randomly assigned to perform the task with and without the exoskeleton, ensuring balanced exposure to each condition. To complete the study protocol, participants attended the laboratory on two separate days, allowing for adequate assessment under exoskeleton and non-exoskeleton conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2 Participants\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSample size calculation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA power analysis using GPower (matched pairs, dz = 0.56, alpha = 0.05, power = 0.8) indicated a required sample size of 22. Accordingly, 21 female participants without prior exoskeleton or industrial task experience could be recruited at or near the VUB campus. Due to poor EMG data (disturbed EMG signals), 7 participants were excluded, resulting in a final sample of 14 women (27 \u0026plusmn; 10 years; 163.5 cm \u0026plusmn; 3.7 cm; 60.7 kg \u0026plusmn; 9.1 kg) for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3 Measurements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSurface Electromyography (sEMG)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003esEMG sensors were placed on the right side of the body, specifically targeting the following muscles: Trapezius (tr), all three heads of the Deltoideus (anterior da, medialis dm, posterior dp), Brachioradialis (br), Biceps brachii (bb), and Triceps brachii (tlh).Before locating the sensors, skin preparation was conducted according to SENIAM guidelines (Hermens et al., 1999; Barbero et al., 2011).To quantify muscle activity, three standardized maximal voluntary isometric contractions (MVC) over 7 seconds were performed for each monitored muscle. The MVC value was calculated as the average peak activity from the two highest-contraction trials. EMG data were collected at a sampling rate of 2000 Hz using the Cometa MiniWave system (Italy).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePassive shoulder exoskeleton characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the Exo4Work passive shoulder exoskeleton was used. This exoskeleton is described in De Bock et al (2022). Although the level of assistance provided by the exoskeleton can be adjusted by changing the pretension of the spring, the exoskeleton provides estimated peak assistance of 3 Nm (de Bock et al., 2022). The exoskeleton is worn like a backpack and features a hip belt, shoulder straps, and a chest belt for secure positioning. The upper arms were secured using Velcro straps, similar to commercially available devices. The exoskeleton was individually adjusted for wearer comfort and adds 3.8 kg of weight to the body.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRPE and body part discomfort\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe subjective assessment of the perceived effort was carried out after each trial using the 100-point Borg scale (0-100). 0 corresponds to a very, very low level of effort, while 100 means a very, very high level of effort (Borg, 1998). The rating refers to the overall perceived exertion. Furthermore, for the RPE to specific body regions such as head and neck, shoulders, arms, upper and lower back, buttocks, thighs, knees, lower legs, and feet, chest region, abdominal region, and front of pelvis, the 10-point Borg Scale was used (Borg, 1982). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSystem Usability Scale\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe System Usability Scale (SUS) is a reliable instrument for measuring user interaction. It consists of ten questions with possible answers ranging from \u0026lsquo;strongly agree\u0026rsquo; to \u0026lsquo;strongly disagree\u0026rsquo;. It provides an overall measure of usability, referring to how easily a user can accomplish their goals when using this device, including user satisfaction and success rates (Bangor et al., 2008; Brooke, 1996).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4 Procedures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon initial arrival, the subject was informed about the protocol and signed an informed consent. All tests took place at the laboratory of the Human Physiology and Sports Physiotherapy Research Group (MFYS, VUB). The experiment consisted of a total of three laboratory visits in which general participant characteristics (e.g., body height and weight, measurements of body parts) were initially recorded (visit 1) and participants were introduced to the experimental protocol, the laboratory environment, and the Exo4Work exoskeleton (familiarization of around 1 hour). Over the following two visits, the participants completed the experimental protocol. Each trial lasted approximately 1.5 hours. In between the first and the second visit, at least 48 hours were scheduled. In between the second and the third laboratory visit, 6 to 9 days were foreseen. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.1 Familiarization\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe first visit to the lab included familiarization with the study protocol and the exoskeleton to get to know the routine and to reduce learning effects throughout the experimental trials. This involved the execution of the overhead precision task with the Exo4Work exoskeleton (Rossini et al., 2021) up to 12 times with a 3-minute break in between each trial.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.2 Experimental protocol\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental trials included the execution of a custom precision task under exoskeleton and non-exoskeleton conditions, following the protocol by De Bock et al. (2022), and based on methods developed by Kim et al. (2018) to evaluate overhead work precision.\u003c/p\u003e\n\u003cp\u003eAfter locating the sEMG sensors and performing the 3 MVC tests, the Exo4Work was applied and adjusted to the participant with the assistance of the investigators. After each trial, the RPE and a local body part discomfort scale were filled out. Additionally, the SUS was filled out at the end of the session with the exoskeleton. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.3 Overhead precision task\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants utilized a Black \u0026amp; Decker electric screwdriver (1.14 kg) to tighten 20 bolts pre-inserted into an aluminium plate positioned overhead (cf. Figure 1).Force sensors and accelerometers were integrated into the overhead working setup to quantify working performance (duration and accuracy) and to facilitate the segmentation of acquired signals.Participants pressed a push button at pelvic crest height, tightened a bolt at overhead height, and pressed the button again to indicate movement initiation (as indicated in Figure 1). Sensorized aluminium and plexiglass plates allowed tracking contact between the screwdriver bit and the bolt, as well as monitoring screwing errors. The appropriate overhead height was determined using the method described by Sood et al. (2007), which calculates hand height with the shoulder and elbow at a 90-degree angle, plus 0.4 times the difference between hand height with the arm fully extended and hand height at the 90-degree angle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.5 Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEMG\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe For consistency, the second trial was used, as the first and last trials often showed poor-quality data. The raw EMG signals were bandpass filtered (Butterworth, 4th order, 20\u0026ndash;500 Hz), rectified, and smoothed using a 100ms root mean square (RMS). Subsequently, the EMG signal quality was visually inspected, and 7 participants were excluded due to excessive noise or artifacts. Signals were then normalized to the MVC values. Movement cycles (from lifting the arm, drilling, lowering the arm) were segmented, excluding irrelevant movements, and averaged per participant using a 200-point interpolation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMuscle Synergies\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the muscle synergies, a non-negative matrix factorization was performed for each participant using three synergies with an imposed factorization rank of three, determined based on the variance accounted for as the criterion for identifying the optimal number of synergies with a range of around 90%. This analysis results in weighted muscle vectors (W) that define the relative contribution of each muscle to a synergy, while H refers to the activation coefficients that capture the time-varying activation level of each synergy across tasks or conditions. Finally, muscle synergies were clustered based on their activation patterns (H) into three distinct clusters via k-means clustering (MATLAB k-means++ algorithm) (Raj \u0026amp; Palaniappan, 2024). Therefore, peak locations and full-width half max were calculated. Reported will also be W, representing the clustering towards the minimal variance in H. The resulting muscle weights were then used for statistical calculations, and the activation patterns were used to describe the movement phases. The data was organized into three clusters, each corresponding to a different phase of the movement.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePerformance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePerformance data, including task, aiming, and drilling duration as well as error integral during aiming and error integral during drilling, were analysed and reported as mean \u0026plusmn; standard deviation (SD), with statistical comparisons conducted using repeated measures ANOVA to assess within-subject differences between Exo and noExo conditions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRPE and SUS\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Borg Scale RPE data was analyzed and reported as mean \u0026plusmn; SD to summarize central tendency and variability for overall, as well as the specific body regions (cf. 2.3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSUS scores were calculated according to Brooke (1996) and Bangor et al. (2008), yielding values ranging from 0 to 100, with 100 representing the best imaginable and highly acceptable usability. Scores below 50 indicate unacceptable usability, and scores between 50-70 are considered marginally acceptable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Procedures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor all statistical analyses, normality of distribution was tested using Kolmogorov-Smirnov. The corresponding comparative test was then selected. The Wilcoxon-signed rank test was employed for the muscle synergy data using W, as the data is clustered towards minimal variance in H, and the RPE data, while repeated measures ANOVA (with Exo and no Exo as within-subject factors) was utilized for the performance data. All analyses were performed using MATLAB 2019. a, Python 3.6, Microsoft Excel 2016, and SPSS 29.0, with alpha set at 0.05.\u003c/p\u003e"},{"header":"3 Results ","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1 Muscle Synergy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 illustrates the mean activation patterns (H) and the weighted muscle (W) of the seven muscles with (E2) and without (E1) the exoskeleton.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 2 shows muscle synergy cluster analysis with and without exoskeleton across three task phases: Cluster 1 (arm elevation), Cluster 2 (drilling), and Cluster 3 (arm lowering). Each cluster presents bar charts of weighted muscle activations for seven muscles and line plots of corresponding activation patterns. Without the exoskeleton, some muscles show higher activation in specific phases, while with the exoskeleton, activations appear more evenly distributed across muscles\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNo Exo (E1)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn Cluster 1 (arm elevation), the muscles \u003cem\u003etr\u003c/em\u003e and \u003cem\u003eda\u003c/em\u003e showed relatively higher mean weights compared to the other muscles, indicating their prominent roles during arm elevation. The other muscles exhibit lower activation levels, though they still contribute to the movement.\u003c/p\u003e\n\u003cp\u003eIn Cluster 2 (drilling phase), muscle weights across all muscles appeared more balanced compared to Cluster 1, suggesting that stabilization is well distributed.\u003c/p\u003e\n\u003cp\u003eIn Cluster 3 (arm returning), the muscle weights of all seven muscles remained relatively consistent, except for the br with higher activation compared to the other involved muscles.\u003c/p\u003e\n\u003cp\u003eOverall, the muscles \u003cem\u003etlh\u0026nbsp;\u003c/em\u003eand \u003cem\u003edm\u0026nbsp;\u003c/em\u003econsistently showed higher levels of activation across clusters compared to other muscles (cf. Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExo (E2)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn Cluster 1 (arm elevation phase), the muscle weights were relatively consistent across all muscles and contributed moderately, indicating a well-distributed muscle engagement.\u003c/p\u003e\n\u003cp\u003eSimilar to Cluster 1, the mean weights across all muscles in Cluster 2 (drilling phase) remained evenly distributed, suggesting that the stabilization and effort required during the drilling task were shared among all muscles.\u003c/p\u003e\n\u003cp\u003eIn Cluster 3 (arm returning phase), the weighted muscles were also balanced, similar to Clusters 1 and 2, with consistent and controlled muscle engagement.\u003c/p\u003e\n\u003cp\u003eFurthermore, the comparison of the muscles between the \u003cem\u003eExo\u003c/em\u003e and \u003cem\u003enoExo\u003c/em\u003e conditions revealed that the muscle weights slightly differ between both conditions. A significant difference was observed for the \u003cem\u003etr\u003c/em\u003e during Cluster 3 (arm returning phase), with lower activation in the \u003cem\u003eExo\u0026nbsp;\u003c/em\u003econdition compared to \u003cem\u003enoExo\u003c/em\u003e condition (\u003cem\u003eZ\u003c/em\u003e = -2.040 and \u003cem\u003ep\u003c/em\u003e = 0.041) (cf. Table 1). This suggests that the exoskeleton reduced the reliance on the M. Trapezius for scapular stabilization and control during the downward movement of the arm. For other muscles and clusters, although the trends in muscle weights differed qualitatively between both conditions, no statistically significant differences were found (cf. Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1. Weighted muscle data.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emuscle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eno Exo [M ± SD]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExo [M ± SD]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ebb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.559 ± 0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.484 ± 0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ebr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.298 ± 0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.376 ± 0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eda\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.458 ± 0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.534 ± 0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.548 ± 0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.614 ± 0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.338 ± 0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.492 ± 0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003etlh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.456 ± 0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.435 ± 0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003etr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.532 ± 0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.432 ± 0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ebb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.236 ± 0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.353 ± 0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ebr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.257 ± 0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.447 ± 0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eda\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.399 ± 0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.270 ± 0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.461 ± 0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.217 ± 0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.530 ± 0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.306 ± 0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003etlh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.438 ± 0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.496 ± 0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003etr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.384 ± 0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.443 ± 0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ebb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.320 ± 0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.454 ± 0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ebr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.686 ± 0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.408 ± 0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eda\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.310 ± 0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.442 ± 0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.240 ± 0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.399 ± 0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.299 ± 0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.403 ± 0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003etlh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.276 ± 0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.335 ± 0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003etr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.246 ± 0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.425 ± 0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: Anterior Deltoid (da), Medial Deltoid (dm), Posterior Deltoid (dp), Biceps Brachii (bb), Long Head of Triceps Brachii (tlh), Brachioradialis (br), Trapezius Descendens (tr),\u003c/em\u003e\u003cem\u003eM = mean, SD = standard deviation, Z = Wilcoxon signed-rank test\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 Performance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall task duration did not differ significantly between conditions, with an average of 2.18 ± 0.25 seconds in the \u003cem\u003enoExo\u0026nbsp;\u003c/em\u003econdition and 2.24 ± 0.24 seconds in the \u003cem\u003eExo\u0026nbsp;\u003c/em\u003econdition (F = 1.231, p = .287, \u003cem\u003eη²\u003c/em\u003e = .086). However, the aiming phase lasted significantly longer when using the exoskeleton (1.46 ± 0.15 sec) compared to the \u003cem\u003enoExo\u0026nbsp;\u003c/em\u003econdition (1.40 ± 0.13 sec), with a moderate effect size (\u003cem\u003eF\u003c/em\u003e = 2.504, \u003cem\u003ep\u003c/em\u003e = .0138, \u003cem\u003eη²\u003c/em\u003e = .161). In contrast, the duration of the drilling phase remained nearly identical between conditions (0.78 ± 0.18 sec vs. 0.782 ± 0.22 sec; \u003cem\u003eF\u003c/em\u003e = .001, \u003cem\u003ep\u003c/em\u003e = .974, \u003cem\u003eη²\u003c/em\u003e = .000).\u003c/p\u003e\n\u003cp\u003eRegarding accuracy, a notable difference was found in the mean error integral during aiming, which was significantly higher in the \u003cem\u003eExo\u0026nbsp;\u003c/em\u003econdition (0.2 ± 0.0) compared to \u003cem\u003enoExo\u0026nbsp;\u003c/em\u003e(0.1 ± 0.0) (\u003cem\u003eF\u003c/em\u003e = 73.393, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; \u003cem\u003eη²\u003c/em\u003e = .850). This indicates a strong impact of the exoskeleton on aiming precision. In contrast, the mean error integral during drilling showed no significant difference between conditions (\u003cem\u003eF\u003c/em\u003e = .636, \u003cem\u003ep\u003c/em\u003e = .439, \u003cem\u003eη²\u003c/em\u003e = .047) (cf. Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2. Performance data.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerformance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003enoExo [M ± SD]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExo [M ± SD]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferences [\u003cem\u003eF, p,\u003c/em\u003e \u003cem\u003eη²\u003c/em\u003e]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003etask duration (sec)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2180.6 ± 251.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2236.0 ± 237.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.231, .287, .086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eduration aiming\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1403.0 ± 131.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1459.7 ± 148.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.504, 0.138, .161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eduration drilling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e777.6 ± 180.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e776.2 ± 222.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001, .974, .000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eerror integral aiming\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1 ± 0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2 ± 0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.393, \u0026lt;.001, .850\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eerror integral drilling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1 ± 0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1 ± 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.636, .439, .047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: M = mean, SD = standard deviation, F = repeated measures ANOVA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3 RPE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 presents the results of RPE on the Borg 100-point scale, comparing two conditions: \u003cem\u003enoExo\u003c/em\u003e (without an exoskeleton) and \u003cem\u003eExo\u003c/em\u003e (with an exoskeleton), as well as the specific RPE for different body regions on the Borg 10-point scale. The overall RPE was slightly lower in the \u003cem\u003eExo\u003c/em\u003e condition (32 ± 13) compared to \u003cem\u003enoExo\u003c/em\u003e (37 ± 16), but this difference was not statistically significant (\u003cem\u003eZ\u003c/em\u003e = -1.384, \u003cem\u003ep\u003c/em\u003e = .166). Similarly, most body regions showed no significant differences in RPE between conditions. However, a notable exception was found for the shoulders, where RPE was significantly lower in the \u003cem\u003eExo\u003c/em\u003e condition (\u003cem\u003eZ\u003c/em\u003e = -2.111, \u003cem\u003ep\u003c/em\u003e = .035).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. RPE results.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003enoExo [M ± SD]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExo [M ± SD]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferences[\u003cem\u003e\u0026nbsp;Z, p]\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRPE overall (0-100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 ± 16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 ± 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.384, .166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHead and neck\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.823, .068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eShoulders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.111, .035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.155, .248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper back\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 ±1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.378, .705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower back\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.000, .317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eButtocks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.732, .083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTights\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.414, .157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnees\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000, 1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower legs and feet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000, 1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChest region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000, 1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbdominal region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.378, .705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFront of pelvis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 ± 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.000, .317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: M = mean, SD = standard deviation, Z = Wilcoxon signed-rank test\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4 SUS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the SUS for the evaluation of the exoskeleton showed total scores ranging from 7.5 to 67.5 (28.4 ± 14.8), indicating usability levels from unacceptable to marginally acceptable.\u003c/p\u003e\n\u003cp\u003eThe results indicate that users found the system challenging to learn, as the statement \u003cem\u003e‘I needed to learn a lot of things before I could get going with this system’\u003c/em\u003e received a high level of agreement. Similarly, many users found the system cumbersome and unnecessarily complex, suggesting difficulties in navigation and ease of use. Confidence in using the system was relatively low, as indicated by the lower scores for \u003cem\u003e‘I felt very confident using the system’\u003c/em\u003e and \u003cem\u003e‘I thought the system was easy to use’.\u003c/em\u003e Additionally, users did not strongly agree that they would like to use the system frequently, implying dissatisfaction with its usability. While the integration of various functions received a neutral rating, the responses suggest inconsistencies within the system, as indicated by the agreement with \u003cem\u003e‘I thought there was too much inconsistency in this system’\u0026nbsp;\u003c/em\u003e(cf. Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFigure 3 shows the SUS results as bar chart with mean ratings and error bars across ten SUS items. Responses range from strongly disagree (1) to strongly agree (5). Participants reported high agreement with needing to learn a lot before use, finding the system cumbersome, inconsistent, and unnecessarily complex. They disagreed with statements that the system was easy to use, quick to learn, or well-integrated. Confidence in using the system and willingness to use it frequently scored low to moderate.\u003c/em\u003e\u003c/p\u003e"},{"header":"4 Discussion ","content":"\u003cp\u003ePhysically demanding tasks involving the upper limbs continue to challenge workplace ergonomics and promote interest in assistive technologies, such as exoskeletons. Given that such tasks increase the risk of arm pain by 18% (Barthelme et al., 2021), exoskeletons have been explored as potential solutions for reducing muscular strain and improving work ergonomics (Desbrosses et al., 2021; Hessinger et al., 2018; Kuber et al., 2022; Musso et al., 2024), while effects on muscle synergy patterns, task performance and usability remain debated (Fournier et al., 2023). The present study offers new insights by evaluating these aspects in female participants, revealing a complex interplay between muscle relief, movement constraints, and user adaptation.\u003c/p\u003e\n\u003cp\u003eA particular strength of this study is its exclusive focus on female users, an underrepresented population in exoskeleton research, which traditionally centers on male subjects (De Bock et al., 2022; Fournier et al., 2023). By filling this gap, our study contributes valuable sex-specific insights into how a passive upper-body exoskeleton, specifically designed for male users, affects neuromuscular coordination, task performance and usability. Our findings not only highlight the potential challenges in usability and performance for female users, but also emphasize the need for more inclusive design approaches in the future development of exoskeletons.\u003c/p\u003e\n\u003cp\u003eConsistent with previous findings (Gillette and Stephensond, 2018; Hessinger et al., 2018; Otten et al., 2018), our results show a significant reduction in muscle activation in the M. Trapezius during arm descent (cluster 3) when using the exoskeleton. This confirms that the passive exoskeleton effectively reduced the reliance on the M. Trapezius for scapular stabilization, as expected (van Engelhoven et al., 2018; Wu et al., 2018). It is important to note that the exoskeleton used in this study is a passive device, designed primarily to support arm elevation and hold the arm lifted, rather than dynamically assisting movements across all phases. Interestingly, despite this intended function, no substantial differences were found in Cluster 1, where the movement involves arm lifting. Instead, the effects were more pronounced in clusters 2 (drilling phase) and 3 (arm lowering phase), where muscle stabilization plays a greater role. Furthermore, our study did not show a uniform reduction in muscle activation, but rather a redistribution of effort. In cluster 2, the balanced activation across muscles suggests a shared stabilizing effort, potentially shaped by the mechanical support of the exoskeleton. This may reflect compensatory strategies, as the exoskeleton’s constraints could alter natural joint dynamics (Theurel and Desbrosses, 2019). The relatively similar mean activations across all muscles support this interpretation and suggest that stabilizing was distributed among them rather than dominated by a single muscle. This balanced pattern might also be the result of individual differences in dominant muscle activation strategies, which, when averaged, appear more uniform. Notably, Tyagi et al. (2023) found that female participants exhibited significant reductions in shoulder muscle activity when using exoskeletons, especially in the deltoideus muscle. Given that women typically have lower absolute muscular strength and different anthropometric proportions than men (McFarland et al., 2022), the fixed support level was excessive for some participants, potentially causing unintended compensatory muscle activation. A kinematic analysis of arm trajectories would help clarify how these mechanical constraints influenced motor control and precision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite muscular benefits, the exoskeleton did not improve task efficiency. Contrary to expectations, aiming duration increased, and aiming precision significantly declined. This aligns with Fournier et al. (2023), who found that exoskeleton effects on productivity vary depending on task demands. One possible explanation is that the exoskeleton restricted natural joint dynamics, requiring participants to adjust their coordination strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch indicates that these mechanical constraints can interfere with fine motor skills and induce postural discomfort (Luger et al., 2023), especially in tasks requiring high precision. Additionally, cognitive load may increase when participants must compensate for restricted movement (De Bock et al., 2022). Thus, while the exoskeleton likely facilitated large motor effort, its rigidity may have impaired the subtle, corrective movements needed for accuracy. Furthermore, adaptation time is crucial in exoskeleton performance outcomes (De Bock et al., 2022). Although our participants underwent a familiarization period, they were not experienced exoskeleton users. Therefore, the observed decline in aiming accuracy may still reflect an early stage learning curve rather than a fundamental limitation of the device. With extended training, performance might improve over time.\u003c/p\u003e\n\u003cp\u003eAgainst this background, usability ratings provide a key explanation for the observed trade-offs between muscle relief and task performance. The low SUS score indicates that participants found the exoskeleton prototype difficult to use. This aligns with Gutierrez et al. (2024), who reported that women often face challenges in exoskeleton adaptation due to suboptimal anthropometric fit, especially in early-stage prototypes. Although some studies (Leibman and Choi, 2023) report no significant sex differences in upper-limb exoskeleton fit, our participants reported discomfort and adjustment difficulties, suggesting that the ergonomic design may still need optimization for female users. This discomfort may explain why overall RPE did not decrease significantly, despite a localized reduction in muscle shoulder activity. As Arauz et al. (2024) emphasized, users often value muscle relief, but poor usability negates these benefits. Participants might have compensated for mechanical constraints by increasing stabilizing effort, thereby counteracting the expected fatigue relief. These findings are consistent with Elprama et al. (2022), who identified comfort, ease of use, and ergonomic fit as key factors for exoskeleton acceptance. Discomfort related to weight, thermal comfort, or fit can limit perceived usefulness and reduce long-term willingness to use the exoskeleton. In our study, negative experiences may reflect low confidence in the exoskeleton’s effectiveness and a lack of task compatibility. Ultimately, while localized muscle relief was achieved, the usability issues overshadowed these benefits, contributing to the overall lack of perceived improvements in RPE.\u003c/p\u003e\n\u003cp\u003eGiven the rising incidence of WMSDs (Govaerts et al., 2021; Overstreet et al., 2023), it is crucial to optimize exoskeletons for both ergonomic support and functional usability. Future research should focus on developing task-specific adaptations, sex-inclusive designs, and understanding long-term adaptation effects to maximize the practical benefits of exoskeletons in industrial and occupational contexts.\u003c/p\u003e\n\u003cp\u003eWhile this study offers valuable insights into female-specific responses to passive upper-limb exoskeleton use, several limitations should be considered. First, participants had limited prior experience with exoskeletons, and the brief familiarization period may have been insufficient for full adaptation. It is possible that the observed reductions in accuracy and perceived usability were influenced by this early stage learning curve. Future studies should incorporate longer training protocols to assess whether motor coordination, user comfort, and performance improve over time. Second, this study examined only a single passive shoulder-support exoskeleton with a fixed level of assistance. While this allows for controlled analysis, it also limits the applicability of the findings to other exoskeleton types.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLastly, the insufficient sample size about the a priori power analysis is an important limitation of this study. This lack of 8 participants compared to the power analysis reduces the statistical significance of the study and consequently limits the informative value and generalizability of the conclusions to be drawn from the data.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our findings highlight the importance of designing exoskeletons that consider sex-specific differences in strength, anthropometry, and movement patterns. While exoskeletons can alleviate muscle strain, they need to support natural movement to avoid unintended compensations. Although the exoskeleton in our study reduced shoulder muscle activity, it negatively impacted aiming accuracy and increased task duration, likely due to mechanical limitations and usability challenges. This underscores the complex relationship between biomechanical relief, motor control, and user experience during exoskeleton-assisted tasks.\u003c/p\u003e\n\u003cp\u003eFuture research should systematically investigate sex-specific differences and adapt exoskeletons more closely to different user groups. Studies with comprehensive evaluation approaches are necessary to better understand the balance between biomechanical relief, precision of movement, and user acceptance and to optimize usability in real working environments.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the local ethics committee as an amendment at the Vrije Universiteit Brussel. All participants signed a written consent for participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript and consent to its publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Brussels Institute of Advanced Studies (Grant: BrIAS2024). Maria Alejandra Diaz was funded by the Federal Public Service for Policy and Support (AidWear project).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJKG: Formal analysis, visualization, writing - original draft; BW: Conceptualization, Methodology, Supervision, Writing – review \u0026amp; editing; MD: Data collection, Formal analysis, Methodology, Writing – review \u0026amp; editing; SDB: Conceptualization, Writing – review \u0026amp; editing; LH: Formal analysis, Writing – review \u0026amp; editing; VD: Technical support; AP: Data collection, Methodology; BR: Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArauz, P.G., Chavez, G., Reinoso, V., Ruiz, P., Ortiz, E., Cevallos, C., Garcia, G., 2024. 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Medicine and science in sports and exercise 14, 377\u0026ndash;381.\u003c/li\u003e\n \u003cli\u003eBrambilla, C., Lavit Nicora, M., Storm, F., Reni, G., Malosio, M., Scano, A., 2023. Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review. Bioengineering 10, 445.\u003c/li\u003e\n \u003cli\u003eBrooke, J., 1996. SUS-A quick and dirty usability scale. Usability evaluation in industry 189, 4\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eda Costa, B.R., Vieira, E.R., 2010. Risk factors for work-related musculoskeletal disorders: A systematic review of recent longitudinal studies. American journal of industrial medicine 53, 285\u0026ndash;323. https://doi.org/10.1002/ajim.20750.\u003c/li\u003e\n \u003cli\u003ede Bock, S., Ampe, T., Rossini, M., Tassignon, B., Lefeber, D., Rodriguez-Guerrero, C., Roelands, B., Geeroms, J., Meeusen, R., de Pauw, K., 2023. Passive shoulder exoskeleton support partially mitigates fatigue-induced effects in overhead work. Applied ergonomics 106, 103903.\u003c/li\u003e\n \u003cli\u003ede Bock, S., Rossini, M., Lefeber, D., Rodriguez-Guerrero, C., Geeroms, J., Meeusen, R., de Pauw, K., 2022. An occupational shoulder exoskeleton reduces muscle activity and fatigue during overhead work. IEEE Transactions on Biomedical Engineering 69, 3008\u0026ndash;3020.\u003c/li\u003e\n \u003cli\u003eDesbrosses, K., Schwartz, M., Theurel, J., 2021. Evaluation of two upper-limb exoskeletons during overhead work: Influence of exoskeleton design and load on muscular adaptations and balance regulation. European Journal of Applied Physiology 121, 2811\u0026ndash;2823.\u003c/li\u003e\n \u003cli\u003eElprama, S.A., Vanderborght, B., Jacobs, A., 2022. An industrial exoskeleton user acceptance framework based on a literature review of empirical studies. 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EMG analysis of an upper body exoskeleton during automotive assembly, 2018 in:\u0026nbsp;Proceedings of the 42nd annual meeting of the American society of biomechanics, pp.\u0026nbsp;308\u0026ndash;309.\u003c/li\u003e\n \u003cli\u003eGovaerts, R., Tassignon, B., Ghillebert, J., Serrien, B., Bock, S. de, Ampe, T., El Makrini, I., Vanderborght, B., Meeusen, R., Pauw, K. de, 2021. Prevalence and incidence of work-related musculoskeletal disorders in secondary industries of 21st century Europe: a systematic review and meta-analysis. BMC musculoskeletal disorders 22, 1\u0026ndash;30.\u003c/li\u003e\n \u003cli\u003eGr\u0026auml;f, J., Grospretre, S., Argubi-Wollesen, A., Wollesen, B., 2024. Impact of a passive upper-body exoskeleton on muscular activity and precision in overhead single and dual tasks: an explorative randomized crossover study. Frontiers in Neurology 15, 1405473.\u003c/li\u003e\n \u003cli\u003eGutierrez, N., Ojelade, A., Kim, S., Barr, A., Akanmu, A., Nussbaum, M.A., Harris-Adamson, C., 2024. Perceived benefits, barriers, perceptions, and readiness to use exoskeletons in the construction industry: Differences by demographic characteristics. Applied ergonomics 116, 104199.\u003c/li\u003e\n \u003cli\u003eHessinger, M., Christmann, E., Werthschutzky, R., Kupnik, M., 2018. User interaction measurements of an exoskeleton using EMG and joint torque. TM-TECHNISCHES MESSEN 85, 487\u0026ndash;495.\u003c/li\u003e\n \u003cli\u003eKim, S., Nussbaum, M.A., Esfahani, M.I.M., Alemi, M.M., Alabdulkarim, S., Rashedi, E., 2018. Assessing the influence of a passive, upper extremity exoskeletal vest for tasks requiring arm elevation: Part I\u0026ndash;\u0026ldquo;Expected\u0026rdquo; effects on discomfort, shoulder muscle activity, and work task performance. Applied ergonomics 70, 315\u0026ndash;322.\u003c/li\u003e\n \u003cli\u003eKuber, P.M., Abdollahi, M., Alemi, M.M., Rashedi, E., 2022. A systematic review on evaluation strategies for field assessment of upper-body industrial exoskeletons: Current practices and future trends. Annals of Biomedical Engineering 50, 1203\u0026ndash;1231.\u003c/li\u003e\n \u003cli\u003eLeibman, D., Choi, H., 2023. Individual Differences in Body Mass, Biological Sex, and Physical Fitness Affecting Human-Exoskeleton Interactions. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 1794\u0026ndash;1800.\u003c/li\u003e\n \u003cli\u003eLuger, T., B\u0026auml;r, M., Seibt, R., Rieger, M.A., Steinhilber, B., 2023. Using a back exoskeleton during industrial and functional tasks\u0026mdash;Effects on muscle activity, posture, performance, usability, and wearer discomfort in a laboratory trial. Human factors 65, 5\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eMcFarland, T.C., McDonald, A.C., Whittaker, R.L., Callaghan, J.P., Dickerson, C.R., 2022. Level of exoskeleton support influences shoulder elevation, external rotation and forearm pronation during simulated work tasks in females. Applied ergonomics 98, 103591.\u003c/li\u003e\n \u003cli\u003e2022. Musculoskeletal health. https://www.who.int/news-room/fact-sheets/detail/musculoskeletal-conditions.\u003c/li\u003e\n \u003cli\u003eMusso, M., Oliveira, A.S., Bai, S., 2024. Influence of an upper limb exoskeleton on muscle activity during various construction and manufacturing tasks. Applied ergonomics 114, 104158.\u003c/li\u003e\n \u003cli\u003eOtten, B.M., Weidner, R., Argubi-Wollesen, A., 2018. Evaluation of a novel active exoskeleton for tasks at or above head level. IEEE Robotics and Automation Letters 3, 2408\u0026ndash;2415.\u003c/li\u003e\n \u003cli\u003eOverstreet, D.S., Strath, L.J., Jordan, M., Jordan, I.A., Hobson, J.M., Owens, M.A., Williams, A.C., Edwards, R.R., Meints, S.M., 2023. A brief overview: sex differences in prevalent chronic musculoskeletal conditions. International journal of environmental research and public health 20, 4521.\u003c/li\u003e\n \u003cli\u003ePenna, M.F., Giordano, L., Tortora, S., Astarita, D., Amato, L., Dell\u0026rsquo;Agnello, F., Menegatti, E., Gruppioni, E., Vitiello, N., Crea, S., 2024. A muscle synergies-based controller to drive a powered upper-limb exoskeleton in reaching tasks. Wearable Technologies 5, e14.\u003c/li\u003e\n \u003cli\u003ePinho, J.P., Forner-Cordero, A., 2022. Shoulder muscle activity and perceived comfort of industry workers using a commercial upper limb exoskeleton for simulated tasks. Applied ergonomics 101, 103718.\u003c/li\u003e\n \u003cli\u003eRaj, Edward Jero Sam Jeeva, Palaniappan, R., 2024. A novel method for movement quality analysis of lower limb joints using surface electromyography signals and k-means clustering technique. Biomedical Signal Processing and Control 95, 106455.\u003c/li\u003e\n \u003cli\u003eRossini, M., Bock, S. de, van der Have, A., Flynn, L., Rodriguez-Cianca, D., Pauw, K. de, Lefeber, D., Geeroms, J., Rodriguez-Guerrero, C., 2021. Design and evaluation of a passive cable-driven occupational shoulder exoskeleton. IEEE Transactions on Medical Robotics and Bionics 3, 1020\u0026ndash;1031.\u003c/li\u003e\n \u003cli\u003eSENIAM. http://seniam.org/sensor_location.htm (accessed 23 November 2021).\u003c/li\u003e\n \u003cli\u003eSood, D., Nussbaum, M.A., Hager, K., 2007. Fatigue during prolonged intermittent overhead work: reliability of measures and effects of working height. Ergonomics 50, 497\u0026ndash;513.\u003c/li\u003e\n \u003cli\u003eTheurel, J., Desbrosses, K., 2019. Occupational exoskeletons: overview of their benefits and limitations in preventing work-related musculoskeletal disorders. IISE Transactions on Occupational Ergonomics and Human Factors 7, 264\u0026ndash;280.\u003c/li\u003e\n \u003cli\u003eTing, L.H., McKay, J.L., 2007. Neuromechanics of muscle synergies for posture and movement. Current opinion in neurobiology 17, 622\u0026ndash;628.\u003c/li\u003e\n \u003cli\u003eTyagi, O., Mukherjee, T.R., Mehta, R.K., 2023. Neurophysiological, muscular, and perceptual adaptations of exoskeleton use over days during overhead work with competing cognitive demands. Applied ergonomics 113, 104097.\u003c/li\u003e\n \u003cli\u003evan Engelhoven, L., Poon, N., Kazerooni, H., Barr, A., Rempel, D., Harris-Adamson, C., 2018 Evaluation of an adjustable support shoulder exoskeleton on static and dynamic overhead tasks, in:\u0026nbsp;Proceedings of the Human Factors and Ergonomics Society Annual Meeting. SAGE Publications Sage CA: Los Angeles, CA, pp.\u0026nbsp;804\u0026ndash;808.\u003c/li\u003e\n \u003cli\u003eWollesen, B., Gr\u0026auml;f, J., Bock, S. de, Alfio, E., D\u0026iacute;az, M.A., Pauw, K. de, 2024a. Gender Differences in Performing an Overhead Drilling Task Using an Exoskeleton\u0026mdash;A Cross-Sectional Study. Biomimetics 9, 601.\u003c/li\u003e\n \u003cli\u003eWollesen, B., Gr\u0026auml;f, J., Hansen, L., Gurevich, A., Elprama, S.A., Argubi-Wollesen, A., Pauw, K. de, 2024b. Gender differences in the use of an upper-extremity exoskeleton during physically and cognitively demanding tasks-a study protocol for a randomized experimental trial. Frontiers in Neurology 15, 1401937.\u003c/li\u003e\n \u003cli\u003eWu, Wen; Fong, Justin; Crocher, Vincent; Lee, Peter V. S.; Oetomo, Denny; Tan, Ying; Ackland, David C. (2018): Modulation of shoulder muscle and joint function using a powered upper-limb exoskeleton. In: Journal of biomechanics 72, S. 7\u0026ndash;16.\u003c/li\u003e\n \u003cli\u003e2019. Work-related musculoskeletal disorders: prevalence, costs and demographics in the EU-European risk observatory report.\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":"Upper-body exoskeleton, muscle synergy, overhead work, user comfort","lastPublishedDoi":"10.21203/rs.3.rs-9019572/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9019572/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Work-related musculoskeletal disorders are common, especially among women performing repetitive overhead tasks. Methods: In a randomized 2x2 crossover study with 14 female participants, we investigated the effects of a passive upper-body exoskeleton during an overhead precision task, involving the tightening of 20 bolts into a sensor-based workstation, while muscle activation, task performance, and usability were assessed. Results: The results showed significant reduced M. Trapezius activations during arm lowering (\u003cem\u003ep\u003c/em\u003e= .041), task duration (\u003cem\u003ep\u003c/em\u003e = .014), and target accuracy (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) when using the exoskeleton. Subjective strain was significantly lower only in the shoulders (\u003cem\u003ep\u003c/em\u003e = .035). The usability was rated as “unacceptable”, with users criticizing the complexity and learning effort. While the exoskeleton reduced muscle load, its mechanical limitations impaired precision and usability, especially for women. Conclusion: These results highlight the importance of sex-specific, ergonomic, and adaptive designs to improve exoskeleton effectiveness and acceptance.\u003c/p\u003e","manuscriptTitle":"Influence of a passive shoulder exoskeleton on drilling performance in women- a cross- sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 12:59:48","doi":"10.21203/rs.3.rs-9019572/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-26T03:03:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T00:20:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T17:15:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310647374070531453171331567505919073756","date":"2026-03-18T02:27:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28732324606369633303970862745124947400","date":"2026-03-15T02:31:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123279189232321597580189433997039015201","date":"2026-03-14T20:29:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268772546532426796975723468865828080561","date":"2026-03-11T10:31:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-09T13:18:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T13:14:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T13:11:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-06T10:00:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-06T09:06:29+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":"72d206d2-7534-4d33-abc2-64ea06518263","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64349959,"name":"Physical sciences/Engineering"},{"id":64349960,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2026-05-11T09:14:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 12:59:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9019572","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9019572","identity":"rs-9019572","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}