研究人在复杂环境下的人机协作生理反应，发现身体努力提升以维持表现。
Multimodal Physiological Assessment of Contact-Rich Physical Human-Robot Interaction Under Varying Environmental Conditions
- 在18种环境组合中同步采集皮肤电活动、肌电和眼动数据
- 温度升高导致自主神经负荷上升，但任务表现不变
- 适合关注人因工程与自适应机器人控制的研究者
现实场景中的人机协作（pHRI）任务常伴随多变的温湿度、噪声和光照条件。传统以任务表现为中心的评估忽略了这些环境压力带来的生理负担。为此，我们在18种温度、声学噪声和照度组合下，开展了包含接触式追踪任务的多模态实证研究，同步记录了皮肤电活动（EDA）、表面肌电（sEMG）、眼动数据及主观环境舒适度评分。结合执行数据评估发现，尽管任务表现稳定，但自主神经负荷（以基线皮肤电导水平SCL衡量）随温度升高而增加，而物理和认知负荷未受影响。感知舒适度与追踪误差或完成时间无显著关联。结果揭示了一种补偿机制：操作员通过提升生理努力来抑制热不适，从而保持任务性能。该发现推动开发基于实时生理指标的智能控制架构，以降低非结构化环境中操作者的整体负荷。
原文摘要 · Abstract (English)
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.
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