Monitoring Fatigue-Induced Changes in Performance during Robot-Mediated Dynamic Movement

Robotic exoskeletons are promising devices capable of both administering therapeutic exercises and assessing human movement quality. Although assessing fatigue is crucial to informing effective strategies for rehabilitation, existing metrics for evaluating fatigue during robot-mediated exercise remain underdeveloped. Current techniques focus on monitoring localized muscle fatigue, but do not consider the complex relationship between changes in muscle activity and associated alterations in joint motion during dynamic movement. In this work, we propose a system-based monitoring paradigm for tracking fatigue-induced changes in performance. The method uses a time-series model to approximate the dynamics of a human-exoskeleton system by mapping muscle activity to movement variables. An index of performance is calculated from modeling errors to continuously track changes in this dynamic relationship over time. Results showed that the index effectively captured fatigue-induced degradation in performance over time during an exoskeleton-administered resistive exercise. The index outperformed a traditional indicator of fatigue that is typically used during robotic intervention, suggesting the proposed approach has the potential to improve fatigue monitoring efforts during robot-aided movement training.