@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{xie_lu_wang_li_xu_2023, title={An Image-Based Human-Robot Collision Avoidance Scheme: A Proof of Concept}, volume={6}, ISSN={["2472-5846"]}, DOI={10.1080/24725838.2023.2222651}, abstractNote={Occupational Applications:In modern industrial plants, collisions between humans and robots pose a significant risk to occupational safety. To address this concern, we sought to devise a reliable system for human-robot collision avoidance system employing computer vision. This system enables proactive prevention of dangerous collisions between humans and robots. In contrast to previous approaches, we used a standard RGB camera, making implementation more convenient and cost-effective. Furthermore, the proposed method greatly extends the effective detection range compared to previous studies, thereby enhancing its utility for monitoring large-scale workplaces.}, journal={IISE TRANSACTIONS ON OCCUPATIONAL ERGONOMICS & HUMAN FACTORS}, author={Xie, Ziyang and Lu, Lu and Wang, Hanwen and Li, Li and Xu, Xu}, year={2023}, month={Jun} } @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. BACKGROUND Human-robot collaboration has been a flourishing work configuration in recent years. Yet, it could lead to work-related musculoskeletal disorders if the collaborative tasks result in awkward postures for workers. METHODS The proposed approach follows two steps: first, a 3D human skeleton reconstruction method was adopted to calculate workers' continuous awkward posture (CAP) score; second, an online gradient-based reinforcement learning algorithm was designed to dynamically improve workers' CAP score by adjusting the positions and orientations of the robot end effector. RESULTS In an empirical experiment, the proposed approach can significantly improve the CAP scores of the participants during a human-robot collaboration task when compared with the scenarios where robot and participants worked together at a fixed position or at the individual elbow height. The questionnaire outcomes also showed that the working posture resulted from the proposed approach was preferred by the participants. CONCLUSION The proposed model-free reinforcement learning method can learn the optimal worker postures without the need for specific biomechanical models. The data-driven nature of this method can make it adaptive to provide personalized optimal work posture. APPLICATION The proposed method can be applied to improve the occupational safety in robot-implemented factories. Specifically, the personalized robot working positions and orientations can proactively reduce exposure to awkward postures that increase the risk of musculoskeletal disorders. The algorithm can also reactively protect workers by reducing the workload in specific joints.}, 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{lu_ghosh_2023, title={Nonparametric Estimation of Multivariate Copula Using Empirical Bayes Methods}, volume={11}, ISSN={["2227-7390"]}, url={https://doi.org/10.3390/math11204383}, DOI={10.3390/math11204383}, abstractNote={In the fields of finance, insurance, system reliability, etc., it is often of interest to measure the dependence among variables by modeling a multivariate distribution using a copula. The copula models with parametric assumptions are easy to estimate but can be highly biased when such assumptions are false, while the empirical copulas are nonsmooth and often not genuine copulas, making the inference about dependence challenging in practice. As a compromise, the empirical Bernstein copula provides a smooth estimator, but the estimation of tuning parameters remains elusive. The proposed empirical checkerboard copula within a hierarchical empirical Bayes model alleviates the aforementioned issues and provides a smooth estimator based on multivariate Bernstein polynomials that itself is shown to be a genuine copula. Additionally, the proposed copula estimator is shown to provide a more accurate estimate of several multivariate dependence measures. Both theoretical asymptotic properties and finite-sample performances of the proposed estimator based on simulated data are presented and compared with some nonparametric estimators. An application to portfolio risk management is included based on stock prices data.}, number={20}, journal={MATHEMATICS}, author={Lu, Lu and Ghosh, Sujit}, year={2023}, month={Oct} } @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} } @misc{lu_xie_wang_li_xu_2022, title={Mental stress and safety awareness during human-robot collaboration - Review}, volume={105}, ISSN={["1872-9126"]}, url={https://doi.org/10.1016/j.apergo.2022.103832}, DOI={10.1016/j.apergo.2022.103832}, abstractNote={Human-robot collaboration (HRC) is an emerging research area that has gained tremendous attention in both academia and industry. Yet, the feature that humans and robots sharing the workplace has led to safety concerns. In particular, the mental stress or safety awareness of human teammates during HRC remains unclear but is also of great importance to workplace safety. In this manuscript, we reviewed twenty-five studies for understanding the relationships between HRC and workers' mental stress or safety awareness. Specifically, we aimed to understand: (1) robot-related factors that may affect human workers' mental stress or safety awareness, (2) a number of measurements that could be used to evaluate workers' mental stress in HRC, and (3) various methods for measuring safety awareness that had been adopted or could be applied in HRC. According to our literature review, robot-related factors including robot characteristics, social touching and trajectory have relationships with workers' mental stress or safety awareness. For the measurement of mental stress and safety awareness, each method mentioned has its validity and rationality. Additionally, a discussion related to the potential co-robot actions to lower mental stress or improve safety awareness as well as future implications were provided.}, journal={APPLIED ERGONOMICS}, publisher={Elsevier BV}, author={Lu, Lu and Xie, Ziyang and Wang, Hanwen and Li, Li and Xu, Xu}, year={2022}, month={Nov} } @article{wang_xie_lu_li_xu_2021, title={A computer-vision method to estimate joint angles and L5/S1 moments during lifting tasks through a single camera}, volume={129}, ISSN={["1873-2380"]}, DOI={10.1016/j.jbiomech.2021.110860}, abstractNote={Weight lifting is a risk factor of work-related low-back musculoskeletal disorders (MSD). From the ergonomics perspective, it is important to measure workers' body motion during a lifting task and estimate low-back joint moments to ensure the low-back biomechanical loadings are within the failure tolerance. With the recent development of advanced deep neural networks, an increasing number of computer vision algorithms have been presented to estimate 3D human poses through videos. In this study, we first performed a 3D pose estimation of lifting tasks using a single RGB camera and VideoPose3D, an open-source library with a fully convolutional model. Joint angle trajectories and L5/S1 joint moment were then calculated following a top-down inverse dynamic biomechanical model. To evaluate the accuracy of the computer-vision-based angular trajectories and L5/S1 joint moments, we conducted an experiment in which participants performed a variety of lifting tasks. The body motions of the participants were concurrently captured by an RGB camera and a laboratory-grade motion tracking system. The body joint angles and L5/S1 joint moments obtained from the camera were compared with those obtained from the motion tracking system. The results showed a strong correlation (r > 0.9, RMSE < 10°) between the two methods for shoulder flexion, trunk flexion, trunk rotation, and elbow flexion. The computer-vision-based method also yielded a good estimate for the total L5/S1 moment and the L5/S1 moment in the sagittal plane (r > 0.9, RMSE < 20 N·m). This study showed computer vision could facilitate safety practitioners to quickly identify the jobs with high MSD risks through field survey videos.}, journal={JOURNAL OF BIOMECHANICS}, author={Wang, Hanwen and Xie, Ziyang and Lu, Lu and Li, Li and Xu, Xu}, year={2021}, month={Dec} } @article{li_prabhu_xie_wang_lu_xu_2021, title={Lifting Posture Prediction With Generative Models for Improving Occupational Safety}, volume={51}, ISSN={["2168-2305"]}, url={https://doi.org/10.1109/THMS.2021.3102511}, DOI={10.1109/THMS.2021.3102511}, abstractNote={Lifting tasks have been identified to be highly associated with work-related low back pain. Posture prediction can be used for simulating workers’ posture of lifting tasks and thus facilitate the prevention of low back pain (LBP). This study adopts two generative models, conditional variational encoder and conditional generative adversarial network, to predict lifting postures. A regular feed-forward neural network (FNN) developed upon previous studies is also investigated for comparison purposes. Ground-truth lifting posture data collected by a motion capture system is used for training and testing the models. The models are trained with datasets of different size and loss functions, and the results are compared. The conditional variational autoencoder and the regular FNN achieved comparable top performance in lifting posture prediction in terms of accuracy and posture validity. Both generative models are able to partially capture the variability of constrained postures. Overall, the results prove that using a generative model is able to predict postures with reasonable accuracy and validity (RMSE of coordinates = 0.049 m; RMSE of joint angles = 19.58$^\circ$). The predicted postures can support biomechanical analysis and ergonomics assessment of a lifting task to reduce the risk of low back injuries.}, number={5}, journal={IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Li, Li and Prabhu, Saiesh and Xie, Ziyang and Wang, Hanwen and Lu, Lu and Xu, Xu}, year={2021}, month={Oct}, pages={494–503} }