构建工业人机协作多模态生理数据集，助力智能机器人感知人类状态。
MultiPhysio-HRC: Multimodal Physiological Signals Dataset for industrial Human-Robot Collaboration
- 融合脑电、心电等七类生理信号与语音、表情数据，真实场景采集。
- 包含认知任务与虚拟现实体验，标注心理状态，支持情绪识别研究。
- 开源数据集适合人机交互、情感计算及工业健康监测研究者使用。
人机协作（HRC）是工业5.0的核心方向，旨在提升工作效率同时保障员工福祉。准确感知人类的心理与生理状态（如压力、认知负荷）对实现自适应、以人为本的机器人系统至关重要。本文提出MultiPhysio-HRC，一个在真实工业人机协作场景中采集的多模态数据集，包含脑电图（EEG）、心电图（ECG）、皮电活动（EDA）、呼吸（RESP）、肌电（EMG）、语音记录及面部动作单元。数据集融合了受控认知任务、沉浸式虚拟现实体验以及人工与机器人辅助的工业拆卸作业，全面捕捉参与者心理状态。通过标准化心理量表获取丰富真实标签。基准模型在压力与认知负荷分类任务上表现良好，验证了该数据集在情感计算与以人为本机器人研究中的潜力。MultiPhysio-HRC已公开，可支持人本自动化、工作场所福祉与智能机器人系统研究。
原文摘要 · Abstract (English)
Human-robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants' mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset's potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems.
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