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.

The torque control of finger joints is important for effective hand rehabilitation after neural disorders such as stroke. This paper presents an approach for accurate torque control of finger joints with UT hand exoskeleton. We present (1) how we obtained an accurate kinematics model, (2) how we built the torque actuation model with Bowden cable SEA, and (3) how we controlled the torque of finger joints with the kinematic model and the Bowden cable SEA. We have validated our approach with a testbed finger and results show that the UT hand exoskeleton accurately controls the torque of finger joints.