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1. Factors Affecting Workers’ Mental Stress in Handover Activities During Human–Robot Collaboration

   https://doi.org/10.1177/00187208241226823  

   Lu, Lu ; Xie, Ziyang ; Wang, Hanwen ; Su, Bingyi ; Jung, Sehee ; Xu, Xu ( January 2024 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   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.

   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.

   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.

   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.

   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.

   The outcome of this study can serve as a guide to design HRC tasks with a low level of workers’ mental stress.

    

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2. The effects of human-robot interaction modality on workers’ mental stress

   https://doi.org/10.1177/21695067231192561  

   Su, Bingyi ; Jung, SeHee ; Lu, Lu ; Wang, Hanwen ; Qing, Liwei ; Xie, Ziyang ; Xu, Xu ( October 2023 , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Advances in robotics have contributed to the prevalence of human-robot collaboration (HRC). Working and interacting with collaborative robots in close proximity can be psychologically stressful. Therefore, it is important to understand the impacts of human-robot interaction (HRI) on mental stress to promote psychological well-being at the workplace. To this end, this study investigated how the HRI presence, complexity, and modality affect psychological stress in humans and discussed possible HRI design criteria during HRC. An experimental setup was implemented in which human operators worked with a collaborative robot on a Lego assembly task, using different interaction paradigms involving pressing buttons, showing hand gestures, and giving verbal commands. The NASA-Task Load Index, as a subjective measure, and the physiological galvanic skin conductance response, as an objective measure, were used to assess the levels of mental stress. 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, the use of certain interaction modalities, such as verbal commands or hand gestures, led to significantly higher mental stress than pressing buttons, while no significant difference on mental stress was found between showing hand gestures and giving verbal commands.

    

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3. Human Workload and Ergonomics during Human-Robot Collaborative Electronic Waste Disassembly

   https://doi.org/10.1109/ICHMS56717.2022.9980828  

   Chen, Yuhao ; Luo, Yue ; Yerebakan, Mustafa Ozkan ; Xia, Shuyan ; Behdad, Sara ; Hu, Boyi ( November 2022 , 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS))

   A rapid rise in the recycling and remanufacturing of end-of-use electronic waste (e-waste) has been observed due to multiple factors including our increased dependence on electronic products and the lack of resources to meet the demand. E-waste disassembly, which is the operation of extracting valuable components for recycling purposes, has received ever increasing attention as it can serve both the economy and the environment. Traditionally, e-waste disassembly is labor intensive with significant occupational hazards. To reduce labor costs and enhance working efficiency, collaborative robots (cobots) might be a viable option and the feasibility of deploying cobots in high-risk or low value-added e-waste disassembly operations is of tremendous significance to be investigated. Therefore, the major objective of this study was to evaluate the effects of working with a cobot during e-waste disassembly processes on human workload and ergonomics through a human subject experiment. Statistical results revealed that using a cobot to assist participants with the desktop disassembly task reduced the sum of the NASA-TLX scores significantly compared to disassembling by themselves (p = 0.001). With regard to ergonomics, a significant reduction was observed in participants’ mean L5/S1 flexion angle as well as mean shoulder flexion angle on both sides when working with the cobot (p < 0.001). However, participants took a significantly longer time to accomplish the disassembly task when working with the cobot (p < 0.001), indicating a trade-off of deploying cobot in the e-waste disassembly process. Results from this study could advance the knowledge of how human workers would behave and react during human-robot collaborative e-waste disassembly tasks and shed light on the design of better HRC for this specific context. 

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4. Improving Workers’ Musculoskeletal Health During Human-Robot Collaboration Through Reinforcement Learning

   https://doi.org/10.1177/00187208231177574  

   Xie, Ziyang ; Lu, Lu ; Wang, Hanwen ; Su, Bingyi ; Liu, Yunan ; Xu, Xu ( May 2023 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   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.

   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.

   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.

   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.

   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.

   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.

    

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5. Human Robot Collaboration for Enhancing Work Activities

   https://doi.org/10.1177/00187208221077722  

   Liu, Li ; Schoen, Andrew J. ; Henrichs, Curt ; Li, Jingshan ; Mutlu, Bilge ; Zhang, Yajun ; Radwin, Robert G. ( March 2022 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Trade-offs between productivity, physical workload (PWL), and mental workload (MWL) were studied when integrating collaborative robots (cobots) into existing manual work by optimizing the allocation of tasks.

   As cobots become more widely introduced in the workplace and their capabilities greatly improved, there is a need to consider how they can best help their human partners.

   A theoretical data-driven analysis was conducted using the O*NET Content Model to evaluate 16 selected jobs for associated work context, skills, and constraints. Associated work activities were ranked by potential for substitution by a cobot. PWL and MWL were estimated using variables from the O*Net database that represent variables for the Strain Index and NASA-TLX. An algorithm was developed to optimize work activity assignment to cobots and human workers according to their most suited abilities.

   Human workload for some jobs decreased while workload for some jobs increased after cobots were reassigned tasks, and residual human capacity was used to perform job activities designated the most important to increase productivity. The human workload for other jobs remained unchanged.

   The changes in human workload from the introduction of cobots may not always be beneficial for the human worker unless trade-offs are considered. Application: The framework of this study may be applied to existing jobs to identify the relationship between productivity and worker tolerances that integrate cobots into specific tasks.

    

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