人間とロボットの協調組立における解釈可能な認知作業負荷評価のための注意と行動手がかりを統合した視覚ベースフレームワーク
A Vision Based Framework Integrating Attention and Action Cues for Interpretable Cognitive Workload Assessment in Human Robot Collaborative Assembly
RGB-D観測とロボット状態から作業者の注意・行動を時系列で表現し、HRC組立中の作業負荷を連続的かつ解釈可能に評価する視覚ベース手法を提案。ギアボックス組立実験で主観評価や心電図と関連を確認。
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著者: Junyan Xionga, Naiyi Feng, Xingke Xia, Qihang Fan, Suchang Chen, Daqiang Guo
分類: cs.RO
原文アブストラクト
The introduction of human-robot collaboration (HRC) in industrial assembly operations is revolutionizing the manufacturing landscape. In this evolving environment, operators are required to seamlessly coordinate their manual tasks with real-time task information and robotic behaviors. These demands fluctuate during operation, yet conventional workload assessments depend on body-worn physiological sensors that complicate practical deployment. Here, we present a vision-based attention--action framework for continuous and interpretable workload-related assessment in HRC assembly. The framework combines RGB-D observations with robot states and calibrated task-related areas to construct a temporally confirmed representation of operator behavior. This representation identifies where task demand is concentrated and explains how it develops when attention and action diverge, the task context changes, or the operator hesitates. We evaluated the framework in a three-level collaborative gearbox assembly experiment with ten participants, using subjective ratings and synchronized physiological signals as independent references. Raw NASA-TLX ratings confirmed increasing perceived workload across conditions, with significant effects on overall workload and its mental and temporal dimensions. The vision-derived HRC-CWL output was significantly associated with ECG-derived features in seven of nine participants with complete correlation data. Synchronized interaction episodes further showed temporal correspondence between detected hesitation and physiological activity. Real-time deployment demonstrated that the framework can operate without requiring operators to wear additional sensors. These findings support HRC-CWL as an interpretable behavioral proxy for cognitive ergonomics analysis and adaptive robot assistance, rather than a direct psychophysiological measure of workload.
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