適切な協働相手の選択：人とロボットの協調組立における認知人間工学のオンライン評価
Pick the Right Co-Worker: Online Assessment of Cognitive Ergonomics in Human-Robot Collaborative Assembly
低コストステレオカメラとAI（頭部姿勢推定・骨格追跡）で作業者の注意分布と上半身の動きを計測し、協働相手（人間または産業用協調ロボット）とのインタラクションによる認知負荷をオンラインで定量評価する枠組みを提案した。
著者: Marta Lagomarsino, Marta Lorenzini, Pietro Balatti, Elena De Momi, Arash Ajoudani
分類: cs.RO, cs.HC
原文アブストラクト
Human-robot collaborative assembly systems enhance the efficiency and productivity of the workplace but may increase the workers' cognitive demand. This paper proposes an online and quantitative framework to assess the cognitive workload induced by the interaction with a co-worker, either a human operator or an industrial collaborative robot with different control strategies. The approach monitors the operator's attention distribution and upper-body kinematics benefiting from the input images of a low-cost stereo camera and cutting-edge artificial intelligence algorithms (i.e. head pose estimation and skeleton tracking). Three experimental scenarios with variations in workstation features and interaction modalities were designed to test the performance of our online method against state-of-the-art offline measurements. Results proved that our vision-based cognitive load assessment has the potential to be integrated into the new generation of collaborative robotic technologies. The latter would enable human cognitive state monitoring and robot control strategy adaptation for improving human comfort, ergonomics, and trust in automation.
