发表机构
University of Tennessee; University of South Carolina（田纳西大学; 南卡罗来纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个在线多模态框架，整合交互力、TCP运动学和皮肤电导等可解释因素，通过路径曲率调整权重，在接触丰富的人机交互中实现透明且准确的连续工作负荷评估，性能媲美最先进学习方法。
AI 中文摘要
接触丰富的人机物理交互（pHRI）对物理交互、运动调节和生理反应提出了随时间变化的需求，这促使对交互工作负荷进行持续评估。本文提出了一种在线多模态评估框架，将交互力/力矩、平面工具中心点（TCP）运动学以及皮肤电导水平（SCL）整合为四个可解释的工作负荷相关因素。它们的相对贡献通过路径曲率进行调整以反映运动需求的变化，并通过任务进度来考虑随时间的逐渐生理变化。该框架在两种导纳控制模式下，针对温度、声噪和照度的18种受控组合，对24名参与者进行了评估。严格的留一受试者（LOSO）评估使用标准化瞳孔直径（$\mathrm{PD}_z$）作为独立的生理参考，并与静态变体和具有代表性的最先进基于学习的基线进行了比较。所提出的框架在30秒分块上与生理参考实现了队列平均Spearman相关系数$\rho_{30}=0.308$，在24名参与者中有23名显示出正的主题级对应关系。其整体性能与最先进的基于学习的基线相当。同时，我们的框架通过明确的工作负荷相关因素和定义的权重规则保持了评估过程的透明性，同时优于相应的固定权重公式。该框架在测试的两种导纳控制模式下也保持了一致的性能。这些结果支持在接触丰富的pHRI中采用透明且可解释的方法进行连续交互工作负荷评估。
英文摘要
Contact-rich physical human--robot interaction (pHRI) imposes time-varying demands associated with physical interaction, motor regulation, and physiological response, motivating continuous assessment of interaction workload. This paper presents an online multimodal assessment framework that integrates interaction wrench, planar tool-center-point (TCP) kinematics, and skin conductance level (SCL) into four interpretable workload-related factors. Their relative contributions are adjusted using path curvature to reflect changes in motion demand and task progression to account for gradual physiological variation over time. The framework was evaluated with 24 participants across 18 controlled combinations of temperature, acoustic noise, and illuminance under two admittance-control modes. Strict leave-one-subject-out (LOSO) evaluation used standardized pupil diameter ($\mathrm{PD}_z$) as an independent physiological reference and included comparisons with static variants and representative state-of-the-art learning-based baselines. The proposed framework achieves a cohort-mean $30\,\mathrm{s}$ block-wise Spearman correlation of $ρ_{30}=0.308$ with the physiological reference, with positive subject-level correspondence in 23 of 24 participants. Its overall performance is comparable to the state-of-the-art learning-based baseline. At the same time, our framework keeps the assessment process transparent through explicit workload-related factors and defined weighting rules, while outperforming the corresponding fixed-weight formulation. The framework also maintains consistent performance across the two tested admittance-control modes. These results support a transparent and interpretable approach to continuous interaction workload assessment in contact-rich pHRI.
