Multimodal Physiological Assessment of Contact-Rich Physical Human-Robot Interaction Under Varying Environmental Conditions
A new study reveals that operators maintain task performance in contact-rich human-robot interaction under varying environmental conditions by increasing physiological effort, particularly autonomic workload, to suppress thermal discomfort. The findings motivate physiology-aware control architectures.
[2606.14969] Multimodal Physiological Assessment of Contact-Rich Physical Human-Robot Interaction Under Varying Environmental Conditions
[Submitted on 12 Jun 2026]
Title:Multimodal Physiological Assessment of Contact-Rich Physical Human-Robot Interaction Under Varying Environmental Conditions
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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.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.14969 [cs.RO]
(or arXiv:2606.14969v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.14969
arXiv-issued DOI via DataCite (pending registration)
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From: Yanyi Chen [view email] [v1] Fri, 12 Jun 2026 21:41:54 UTC (4,905 KB)
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