Multimodal Physiological Assessment of Contact-Rich Physical Human-Robot Interaction Under Varying Environmental Conditions

2026-06-12

AI Summary

This study addresses a critical gap in traditional task-centric evaluations by investigating the implicit physiological burden imposed on human operators during physical human-robot interaction under varying real-world environmental conditions—specifically temperature, sound, and lighting. Conducting contact-based tracking tasks across 18 environmental combinations, the research simultaneously recorded electrodermal activity (EDA), surface electromyography (sEMG), eye-tracking metrics, and subjective comfort ratings alongside task performance measures. The findings reveal, for the first time, that operators compensate for thermal discomfort by increasing autonomic nervous system load—evidenced by elevated skin conductance levels—to sustain task performance. Notably, while performance remained stable, high temperatures substantially increased physiological cost, and subjective comfort showed no significant correlation with task error or completion time. These insights provide a foundational basis for developing physiology-aware human-robot control systems.

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.

Problem

• physical human-robot interaction
• environmental stressors
• physiological workload
• multimodal assessment
• contact-rich tasks

Innovation

• multimodal physiological assessment
• physical human-robot interaction
• environmental stressors
• physiology-aware control
• autonomic workload