@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. Background Human–robot collaboration (HRC) is gaining attention in industry and academia. Understanding robot-related factors causing mental stress is crucial for designing collaborative tasks that minimize workers’ stress. Methods Mental stress in HRC tasks was measured subjectively through self-reports and objectively through galvanic skin response (GSR) and electromyography (EMG). Robot-related factors including approach direction, movement speed, and trajectory were analyzed. Results Movement speed and approach direction had significant effects on subjective ratings, EMG, and GSR. High-speed and approaching from one side consistently resulted in higher fear, lower comfort, and predictability, as well as increased EMG and GSR signals, indicating higher mental stress. Movement trajectory affected GSR, with the sudden stop condition eliciting a stronger response compared to the constrained trajectory. Interaction effects between speed and approach direction were observed for “surprise” and “predictability” subjective ratings. At high speed, approach direction did not significantly differ, but at low speeds, approaching from the side was found to be more surprising and unpredictable compared to approaching from the front. Conclusion The mental stress of workers during HRC is lower when the robot’s end effector (1) approaches a worker within the worker’s field of view, (2) approaches at a lower speed, or (3) follows a constrained trajectory. Application The outcome of this study can serve as a guide to design HRC tasks with a low level of workers’ mental stress. }, 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{jung_wang_su_lu_qing_fang_xu_2024, title={Learning Undergraduate Data Science Through a Mobile Device and Full Body Movements}, volume={11}, ISSN={["1559-7075"]}, DOI={10.1007/s11528-024-01026-0}, journal={TECHTRENDS}, author={Jung, Sehee and Wang, Hanwen and Su, Bingyi and Lu, Lu and Qing, Liwei and Fang, Xiaolei and Xu, Xu}, year={2024}, month={Nov} }
 @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}, 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{qing_su_jung_lu_wang_xu_2024, title={Predicting Human Postures for Manual Material Handling Tasks Using a Conditional Diffusion Model}, volume={54}, ISSN={["2168-2305"]}, url={https://doi.org/10.1109/THMS.2024.3472548}, DOI={10.1109/THMS.2024.3472548}, number={6}, journal={IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS}, author={Qing, Liwei and Su, Bingyi and Jung, Sehee and Lu, Lu and Wang, Hanwen and Xu, Xu}, year={2024}, month={Dec}, pages={723–732} }
 @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} }