David Saucier

Background: Work-related musculoskeletal disorders (WMSDs) are a significant concern in industrial settings, where repetitive and awkward movements contribute to ergonomic risk. Quantifying this risk allows for a better understanding of operator movement patterns and potential adjustments that can aid in injury risk mitigation. Objective: Traditional observational methods, specifically the Rapid Upper Limb Assessment (RULA), provide ergonomic risk assessments but are limited by observer subjectivity and an inability to calculate movement durations. With wearable technology, precise kinematic and temporal data can be collected. The objective of this paper is to determine the feasibility of using wearable technology in place of video-based assessment for conducting ergonomic assessments. Methods: To compare the effectiveness of these two methods, data was collected on an operator simultaneously by a smart compression sleeve and a video-based assessment system. This data was analyzed to calculate a risk score based on the RULA, which is used to provide an in-depth understanding of operator movements. Results: The video-based assessment system produced an assessment score of 3 for both trials, whereas the smart compression sleeve produced an assessment score of 2.62 for trial 1 and 2.93 for trial 2. Conclusions: The wearable compressive sleeve demonstrated feasibility in the single-subject pilot study, with the ability to provide near-constant measurement if desired. A more comprehensive understanding of operator kinematics can aid in lowering ergonomic risk and therefore helping to mitigate potential WMSDs. Further research is needed to continue to refine the data processing efficiency and feasibility of long-term use.

Accurate and unobtrusive measurement of upper-limb kinematics is critical for advancing wearable sensing technologies used in industrial ergonomics, human–machine interaction, and real-time biomechanics monitoring. This study evaluates the performance of two soft, flexible wearable sensors—BendLabs biaxial angular displacement sensors and StretchSense capacitive stretch sensors—for quantifying wrist and elbow motions during simulated dynamic industrial tasks. Wrist flexion–extension and radial–ulnar deviation were measured using BendLabs sensors mounted on the dorsal hand, while elbow flexion–extension was captured using StretchSense sensors positioned along the elbow joint. A multi-camera optical motion capture system served as the reference standard. Sensor data were preprocessed using baseline correction, smoothing, denoising, and normalized cross-correlation techniques to support temporal alignment with motion-capture recordings. Across all activities, the BendLabs sensors demonstrated moderate agreement with motion capture for wrist kinematics, with generally better performance for radial–ulnar deviation than for flexion–extension. StretchSense sensors demonstrated stronger agreement with motion capture for elbow flexion–extension, with performance that was generally consistent across task types. These findings support the feasibility of soft wearable sensors for capturing upper-limb kinematics during simulated occupational tasks and highlight their potential for integration into ergonomic assessment, occupational monitoring systems, and future industrial wearable platforms.

Assessing athletic performance through vertical jump metrics, such as jump height (JH) and reactive strength index (RSI), is common in sports performance and rehabilitation settings. Traditionally, force plates have been the gold standard for these measurements, but wearable technology offers potentially more accessible alternatives. This study aimed to evaluate the reliability and validity of pressure sensor-embedded socks, compared with force plates, for measuring contact time, flight time, JH, RSI, and modified RSI (mRSI) during countermovement jumps (CMJ). Twenty active individuals performed CMJs while wearing the socks, and force plate data were simultaneously collected. The results indicated that the force plates showed generally acceptable reliability, while the socks showed questionable reliability. Regarding validity, the socks appeared similar to the force plate data, but this should be interpreted with caution given the lower reliability metrics observed in the socks. Thus, the present study suggests that while sensor socks show promise for field-based assessments, improvements in sensor technology and data processing, such as higher sampling rates, are needed to improve reliability. These findings indicate that wearable technology, such as sensor-embedded socks, can serve as a viable alternative for data measurement in sports science when traditional force plates are not accessible. However, while these socks appear valid compared to force plates, their performance may not always be consistent or reliable.

This study seeks to determine the efficacy of a novel, virtual reality (VR)-based sensorimotor assessment tool, VIST Neuro-ID, in comparison to the gold standard. This was achieved through computing common postural sway metrics, as well as comparing these metrics across population groups including sex and age (50–60 vs. 61–75). Sensorimotor assessments were conducted within the VIST Neuro-ID VR software while participants stood on a force plate. A proxy for center-of-pressure measurement was developed using the six-degree-of-freedom data collected from the head-mounted display used with the VR system. Moderate-to-high (r = 0.542–0.906) Pearson’s correlations were found between VIST Neuro-ID and the force plate for all eight postural sway metrics that were computed. Both systems detected significant differences (p < 0.05) across age groups for all metrics, except for two-dimensional path length from the force plate. Several significant differences were found across sexes, including AP and resultant sway velocity from the force plate, and resultant and AP root-mean-square from the HTC Vive Pro Eye. This indicates potential for VR to be used to collect vital postural sway metrics needed for assessing patient function, while also highlighting potential to identify balance patterns related to aging.

Background: This study was performed to validate the addition of capacitive-based pressure sensors to an existing smart sock developed by the research team. This study focused on evaluating the accuracy of soft robotic sensor (SRS) pressure data and its relationship with laboratory-grade Kistler force plates in collecting ground force reaction data. Methods: Nineteen participants performed walking trials while wearing the smart sock with and without shoes. Data was collected simultaneously with the sock and the force plates for each gait phase including foot-flat, heel-off, and midstance. The correlation between the smart sock and force plates was analyzed using Pearson’s correlation coefficient and R-squared values. Results: Overall, the strength of the relationship between the smart sock’s SRS data and the vertical ground reaction force (GRF) data from the force plates showed a strong correlation, with a Pearson’s correlation coefficient of 0.85 ± 0.1; 86% of the trials had a value higher than 0.75. The linear regression models also showed a strong correlation, with an R-squared value of 0.88 ± 0.12, which improved to 0.90 ± 0.07 when including a stretch-SRS for measuring ankle flexion. Conclusions: With these strong correlation results, there is potential for capacitive pressure sensors to be integrated into the proposed device and utilized in telehealth and sports performance applications.

This study investigates the neurophysiological and biomechanical factors contributing to successful basketball throw performance in novice athletes, utilizing electroencephalography (EEG) and motion capture (MoCap) to analyze joint angles, ground reaction forces (GRFs), and brain activity. Sixteen participants performed basketball throws while EEG and MoCap systems recorded data on movement mechanics and neural activity. Biomechanical findings revealed that successful trials were characterized by refined movements, reduced wrist extension, increased elbow flexion, and more stable foot positioning compared to unsuccessful trials (all p > 0.05), contributing to greater shot accuracy. Reduced movement variability in successful trials further indicated improved motor consistency, reflective of skill development. EEG results showed higher beta and gamma power in the temporal lobe during successful compared to unsuccessful trials (p < 0.05), suggesting increased engagement in visuomotor integration and neural efficiency. Notably, our novice participants demonstrated limited neural efficiency in frontal regions (p > 0.05), potentially due to cognitive interference and self-monitoring. These findings highlight the importance of coordinated biomechanical execution and neural efficiency in optimizing basketball performance. The insights gained have practical implications for designing training interventions that improve motor performance, particularly for novice athletes.

The purpose of this study is to examine Achilles Tendon (AT) rupture and prior injury in the National Basketball Association (NBA). Publicly available data was sourced via two online databases and filtered to identify those reported as having sustained AT rupture. A full injury history was then generated for those players. A total of 51 AT ruptures were identified. Thirty-five per cent (18) had previously missed at least one game for "Achilles tendinitis" or "Achilles tendon injury" on the side of the affected limb. A further 18% were noted to have suffered plantar fasciitis or foot inflammation (9) or previous ankle injury (9). Seven previous injuries were knee related, and five were described as calf muscle injuries. The remaining three had prior soft tissue injuries to the quadriceps, hamstring or groin. Understanding any potential association between previous small time-loss injuries and large time-loss injuries such Achilles tendon ruptures is an important step in advancing injury risk mitigation strategies in professional basketball.

The purpose of this study was to examine changes in ground reaction forces during drop jumps (DJs) and repeated jumps (RJs) across a 10-week offseason strength and conditioning program in collegiate Division I American football players. Twenty-two NCAA Division I American football players were recruited for this study, however, a total of n = 19 subjects (mean ± 95% confidence interval, height=186.83±2.66 cm, body mass=102.73±8.79 cm) completed the study. Subjects visited the laboratory twice, once for pre- and once for post-offseason testing. Subjects performed DJs and RJs during each visit. The DJ was performed off a 30 cm box. The RJ protocol consisted of four consecutive jumps. Ground reaction forces were collected during all jumps. Dependent samples t-tests compared changes in DJ metrics, while repeated measures analyses of variance compared changes in RJ metrics. For the DJ, peak braking power, peak propulsive power, mean propulsive force, and jump height increased (p≤0.027, d≥0.552). For jump 1 of the RJ, peak braking power increased (p=0.005, d=0.733). For all jumps of the RJ, peak propulsive power, mean propulsive force, jump height, and reactive strength index increased (p≤0.049, η2≥0.134). This study demonstrated that DJ and RJ performance increased in 10-weeks of strength and conditioning training. Strength and conditioning coaches may benefit from including DJ and RJ assessments to monitor the effectiveness of the strength and conditioning program.

OCCUPATIONAL APPLICATIONSErgonomists and engineers often look to technology to improve workplace outcomes such as safety and productivity. However, hesitancy toward new technology can hinder the adoption of devices such as wearables. In this study, we spoke to industry representatives about the factors that influence the implementation of wearables in the manufacturing workplace. Focus group participants provided several actionable insights to reduce hesitancy in adopting wearables in the industrial workplace. First, emphasizing safety culture and the benefits to workplace safety and health is paramount. Second, the case for using wearable for performance improvement should also be discussed after leading with safety. Finally, organizations should consider voluntary participation in a wearables program with early adopters.

Detecting and localizing large obstacles, particularly trees in unpaved regions, holds significant importance in autonomy, navigation, and related fields. Equally crucial is the extraction of detailed physical information from sensor captures. Accurately estimating the physical parameters of trees, such as the tree diameter at breast height (DBH), is particularly valuable for commercial and research purposes, especially in forestry and ecological studies. This estimation also plays a pivotal role in navigational tasks within densely vegetated regions, where overcoming obstacles becomes essential to achieving objectives. Achieving the required accuracy often entails labor-intensive processes such as manual collection aiming, data segmentation, or shape-building through mapping. In this context, we propose an algorithm based on particle swarm optimization (PSO) assisted Hough Transformation (HT) for tree DBH estimation, utilizing solely the physical spatial information from an actively available LiDAR point cloud. As a point of comparison, a straightforward circular HT-based method is also implemented. Our proposed approach surpasses the base HT method, demonstrating superior performance with an average error of 5.60 cm and RMSE of 6.57 cm, all while maintaining low time costs. These results reveal promising implications for this research direction in real-world applications, particularly in push-through navigation scenarios.