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This study investigates how environmental stressors like temperature affect the physiological workload of humans operating robots in contact-rich scenarios. While task performance remained stable, operators compensated for thermal discomfort by increasing autonomic effort, a finding that could inform future physiology-aware control architectures and safety protocols. The research highlights critical gaps between objective metrics and human biological costs in real-world robotics deployment.
Multimodal Physiological Assessment of Contact-Rich Physical Human-Robot Interaction Under Varying Environmental Conditions
arXiv:2606.14969v1 Announce Type: new Abstract: Physical human-robot interaction (pHRI) in real-world settings exposes operators to fluctuating environmental conditions during contact-rich tasks. Traditional task-centric evaluations overlook the physiological burdens imposed by these stressors. Therefore, we conducted a multimodal empirical study involving contact-rich tracing tasks under 18 distinct combinations of temperature, acoustic noise, and illuminance. Synchronously, we recorded electrodermal activity (EDA), surface electromyography (sEMG), eye-tracking data, and subjective environmental comfort ratings. Evaluating these physiological signals alongside execution data revealed hidden physiological costs not captured by objective performance. The results revealed that task performance remained stable across all environmental conditions. Autonomic workload, indexed by tonic skin conductance level (SCL), increased with temperature, while physical and cognitive workload were unaffected. Perceived environmental comfort showed no significant association with tracing error or completion time. These findings reveal a compensatory mechanism where operators maintain consistent performance by increasing their physiological effort to suppress thermal discomfort. Such insight motivates the development of physiology-aware control architectures that leverage real-time physiological metrics to reduce operator workload in unstructured environments.
Abstract:Physical human-robot interaction (pHRI) in real-world settings exposes operators to fluctuating environmental conditions during contact-rich tasks. Traditional task-centric evaluations overlook the physiological burdens imposed by these stressors. Therefore, we conducted a multimodal empirical study involving contact-rich tracing tasks under 18 distinct combinations of temperature, acoustic noise, and illuminance. Synchronously, we recorded electrodermal activity (EDA), surface electromyography (sEMG), eye-tracking data, and subjective environmental comfort ratings. Evaluating these physiological signals alongside execution data revealed hidden physiological costs not captured by objective performance. The results revealed that task performance remained stable across all environmental conditions. Autonomic workload, indexed by tonic skin conductance level (SCL), increased with temperature, while physical and cognitive workload were unaffected. Perceived environmental comfort showed no significant association with tracing error or completion time. These findings reveal a compensatory mechanism where operators maintain consistent performance by increasing their physiological effort to suppress thermal discomfort. Such insight motivates the development of physiology-aware control architectures that leverage real-time physiological metrics to reduce operator workload in unstructured environments.
Robotics (cs.RO)
arXiv:2606.14969 [cs.RO]
(or arXiv:2606.14969v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.14969 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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