@article{su_jung_lu_wang_qing_xu_2024, title={Exploring the impact of human-robot interaction on workers' mental stress in collaborative assembly tasks}, volume={116}, ISSN={["1872-9126"]}, url={https://doi.org/10.1016/j.apergo.2024.104224}, DOI={10.1016/j.apergo.2024.104224}, abstractNote={Advances in robotics have contributed to the prevalence of human-robot collaboration (HRC). However, working and interacting with collaborative robots in close proximity can be psychologically stressful. Therefore, understanding the impacts of human-robot interaction (HRI) on mental stress is crucial for enhancing workplace well-being. To this end, this study investigated how the HRI factors – presence, complexity, and modality – affect the psychological stress of workers. We employed both the NASA-Task Load Index for subjective assessment and physiological metrics including galvanic skin responses, electromyography, and heart rate for objective evaluation. An experimental setup was implemented in which human operators worked together with a collaborative robot on Lego assembly tasks, using different interaction paradigms including pressing buttons, showing hand gestures, and giving verbal commands. The results revealed that the introduction of interactions during HRC helped reduce mental stress and that complex interactions resulted in higher mental stress than simple interactions. Meanwhile, using hand gestures led to significantly higher mental stress than pressing buttons and verbal commands. The findings provided practical insights for mitigating mental stress in the workplace and promoting wellness in the era of HRC.}, journal={APPLIED ERGONOMICS}, author={Su, Bingyi and Jung, SeHee and Lu, Lu and Wang, Hanwen and Qing, Liwei and Xu, Xu}, year={2024}, month={Apr} }
 @article{lu_xie_wang_su_jung_xu_2024, title={Factors Affecting Workers' Mental Stress in Handover Activities During Human-Robot Collaboration}, volume={1}, ISSN={["1547-8181"]}, url={https://doi.org/10.1177/00187208241226823}, DOI={10.1177/00187208241226823}, abstractNote={Objective This study investigated the effects of different approach directions, movement speeds, and trajectories of a co-robot’s end-effector on workers’ mental stress during handover tasks. }, journal={HUMAN FACTORS}, author={Lu, Lu and Xie, Ziyang and Wang, Hanwen and Su, Bingyi and Jung, Sehee and Xu, Xu}, year={2024}, month={Jan} }
 @article{wang_su_lu_jung_qing_xie_xu_2024, title={Markerless gait analysis through a single camera and computer vision}, volume={165}, ISSN={["1873-2380"]}, DOI={10.1016/j.jbiomech.2024.112027}, abstractNote={The assessment of gait performance using quantitative measures can yield crucial insights into an individual's health status. Recently, computer vision-based human pose estimation has emerged as a promising solution for markerless gait analysis, as it allows for the direct extraction of gait parameters from videos. This study aimed to compare the lower extremity kinematics and spatiotemporal gait parameters obtained from a single-camera-based markerless method with those acquired from a marker-based motion tracking system across a healthy population. Additionally, we investigated the impact of camera viewing angles and distances on the accuracy of the markerless method. Our findings demonstrated a robust correlation and agreement (Rxy > 0.75, Rc > 0.7) between the markerless and marker-based methods for most spatiotemporal gait parameters. We also observed strong correlations (Rxy > 0.8) between the two methods for hip flexion/extension, knee flexion/extension, hip abduction/adduction, and hip internal/external rotation. Statistical tests revealed significant effects of viewing angles and distances on the accuracy of the identified gait parameters. While the markerless method offers an alternative for general gait analysis, particularly when marker use is impractical, its accuracy for clinical applications remains insufficient and requires substantial improvement. Future investigations should explore the potential of the markerless system to measure gait parameters in pathological gaits.}, journal={JOURNAL OF BIOMECHANICS}, author={Wang, Hanwen and Su, Bingyi and Lu, Lu and Jung, Sehee and Qing, Liwei and Xie, Ziyang and Xu, Xu}, year={2024}, month={Mar} }
 @article{xie_lu_wang_su_liu_xu_2023, title={Improving Workers' Musculoskeletal Health During Human-Robot Collaboration Through Reinforcement Learning}, volume={5}, ISSN={["1547-8181"]}, url={https://doi.org/10.1177/00187208231177574}, DOI={10.1177/00187208231177574}, abstractNote={Objective This study aims to improve workers’ postures and thus reduce the risk of musculoskeletal disorders in human-robot collaboration by developing a novel model-free reinforcement learning method. }, journal={HUMAN FACTORS}, author={Xie, Ziyang and Lu, Lu and Wang, Hanwen and Su, Bingyi and Liu, Yunan and Xu, Xu}, year={2023}, month={May} }
 @article{wang_xie_lu_su_jung_xu_2022, title={A mobile platform-based app to assist undergraduate learning of human kinematics in biomechanics courses}, volume={142}, ISSN={["1873-2380"]}, DOI={10.1016/j.jbiomech.2022.111243}, abstractNote={Whole-body biomechanics examines different physical characteristics of the human body movement by applying principles of Newtonian mechanics. Therefore, undergraduate biomechanics courses are highly demanding in mathematics and physics. While the inclusion of laboratory experiences can augment student comprehension of biomechanics concepts, the cost and the required expertise associated with experiment equipment can be a burden of offering laboratory sessions. In this study, we developed a mobile app to facilitate learning human kinematics in biomechanics curriculums. First, a mobile-based computer-vision algorithm that is based on Convolutional pose machine (CPM), MobileNet V2, and TensorFlow Lite framework is adopted to reconstruct 2D human poses from the images collected by a mobile device camera. Key joint locations are then applied to the human kinematics variable estimator for human kinematics analysis. Simultaneously, students can view various kinematics data for a selected joint or body segment in real-time through the user interface of the mobile device. The proposed app can serve as a potential instructional tool to assist in conducting human motion experiments in biomechanics courses.}, journal={JOURNAL OF BIOMECHANICS}, author={Wang, Hanwen and Xie, Ziyang and Lu, Lu and Su, Bingyi and Jung, Sehee and Xu, Xu}, year={2022}, month={Sep} }