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1. Sharing Stress With a Robot: What Would a Robot Say?

   https://doi.org/10.30658/hmc.1.8  

   Ling, Honson; Björling, Elin (February 2020, Human-Machine Communication)

   With the prevalence of mental health problems today, designing human-robot interaction for mental health intervention is not only possible, but critical. The current experiment examined how three types of robot disclosure (emotional, technical, and by-proxy) affect robot perception and human disclosure behavior during a stress-sharing activity. Emotional robot disclosure resulted in the lowest robot perceived safety. Post-hoc analysis revealed that increased perceived stress predicted reduced human disclosure, user satisfaction, robot likability, and future robot use. Negative attitudes toward robots also predicted reduced intention for future robot use. This work informs on the possible design of robot disclosure, as well as how individual attributes, such as perceived stress, can impact human robot interaction in a mental health context. 

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2. Assessing Workers’ Mental Stress in Hand-over Activities during Human-robot Collaboration

   https://doi.org/10.1177/1071181322661194  

   Lu, Lu; Xie, Ziyang; Wang, Hanwen; Su, Bingyi; Edward P. Fitts, Xu Xu (September 2022, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Human-robot collaboration (HRC) is an emerging research area that has gained tremendous attention from both academia and industry. Since some robot-related factors can elicit mental stress or have negative psychological effects on human workers, it is essential to understand these factors and maintain workers’ mental stress at a low level. Galvanic Skin Response (GSR) measures skin conductance and is known to be a physiological measurement that reflects short-term mental stress. Typically, skin conductance increases in response to greater mental stress. In this study, the mental stress caused by the hand-over activities of a collaborative robot was investigated using both GSR as an objective measurement and NASA-Task Load Index (TLX) as a subjective assessment. Several robot-related factors that may lead to mental stress were experimentally examined. GSR outcomes indicated that end effector approaching within workers’ view, low end effector speed, and constrained end effector trajectory led to a significantly lower skin conductance. Some aspects of the NASA-TLX also indicated that speed and trajectory significantly affected the scores. Yet, no significant differences were found between approaching directions regarding NASA-TLX scores. 

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3. 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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4. HARMONIC: A multimodal dataset of assistive human–robot collaboration

   https://doi.org/10.1177/02783649211050677  

   Newman, Benjamin_A; Aronson, Reuben_M; Srinivasa, Siddhartha_S; Kitani, Kris; Admoni, Henny (December 2021, The International Journal of Robotics Research)

   We present the Human And Robot Multimodal Observations of Natural Interactive Collaboration (HARMONIC) dataset. This is a large multimodal dataset of human interactions with a robotic arm in a shared autonomy setting designed to imitate assistive eating. The dataset provides human, robot, and environmental data views of 24 different people engaged in an assistive eating task with a 6-degree-of-freedom (6-DOF) robot arm. From each participant, we recorded video of both eyes, egocentric video from a head-mounted camera, joystick commands, electromyography from the forearm used to operate the joystick, third-person stereo video, and the joint positions of the 6-DOF robot arm. Also included are several features that come as a direct result of these recordings, such as eye gaze projected onto the egocentric video, body pose, hand pose, and facial keypoints. These data streams were collected specifically because they have been shown to be closely related to human mental states and intention. This dataset could be of interest to researchers studying intention prediction, human mental state modeling, and shared autonomy. Data streams are provided in a variety of formats such as video and human-readable CSV and YAML files. 

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5. Task Interdependence in Human-Robot Teaming

   Fangyun Zhao, Curt Henrichs (August 2020, IEEE International Conference on Robot and Human Interactive Communication)

   Human-robot teaming is becoming increasingly common within manufacturing processes. A key aspect practitioners need to decide on when developing effective processes is the level of task interdependence between human and robot team members. Task interdependence refers to the extent to which one’s behavior affects the performance of others in a team. In this work, we examine the effects of three levels of task interdependence—pooled, sequential, reciprocalin human-robot teaming on human worker’s mental states, task performance, and perceptions of the robot. Participants worked with the robot in an assembly task while their heart rate variability was being recorded. Results suggested human workers in the reciprocal interdependence level experienced less stress and perceived the robot more as a collaborator than other two levels. Task interdependence did not affect perceived safety. Our findings highlight the importance of considering task structure in human-robot teaming and inform future research on and industry practices for human-robot task allocation. 

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