Fatigue Detection for Human Aware Adaptation in Human-Robot Collaboration

Abstract

Knowledge about human cognitive and physical state is a key factor in physical Human-robot collaboration (pHRC). Such information benefits the robot in planning an adaptive control strategy to prevent or mitigate human fatigue. In this paper, we present a method to detect upper limb muscle fatigue during pHRC using a low-cost myoelectric sensor. We used Riemann geometry to extract robust features from the time-series data and designed a classifier to detect the binary state of human fatigue i.e. fatigued vs not fatigued. We evaluated the method using a fine-motor coordination task for the human to guide an industrial robot along a virtual path for sometime followed by a muscle curl exercise until it induces fatigue in the muscles, and then repeat the robot experiment. We recruited nine participants for the study and recorded muscle activity from their dominant upper limb using the myoelectric sensor and used the data to develop a classifier. We compared the accuracy and robustness of the classifier against conventional time-domain and wavelet-based features and showed that Riemann geometry-based features yield higher classification accuracy (∼ 91%) compared to conventional features and require less computational effort. Such classifier can be used in real-time to develop a human-aware adaptation strategy to prevent fatigue.