Muscle Fatigue-Aware Controller for a Semi-Rigid Knee Exoskeleton (I)

Abstract

Wearable assistive devices that monitor muscle fatigue reduce the risk of work-related musculoskeletal disorders, enhance rehabilitation outcomes, and extend operational time by optimizing the power consumption of the device. This work proposes a muscle fatigue-aware controller (MFAC) for a semi-rigid knee exoskeleton. During an offline calibration phase, we use Gaussian Process Regression (GPR) to model the relationship between muscle activation (measured via EMG) and the corresponding joint moment and angle, enabling fatigue state estimation for the controller. The trained model then approximates muscle activation online using only joint states and moment derived from user’s kinematic data and ground reaction forces provided by the wearable device. The estimated muscle activation is used to assess the muscle fatigue state through a model-based fatigue evaluation module. Notably, EMG measurement is only required during the offline training in our approach, enabling EMG-free online estimation, which significantly enhances the feasibility for long-term mobile applications. Building on muscle fatigue and human-exoskeleton interaction models, we then developed an adaptive controller within a predictive control framework. The resulting optimization problem generates control signals that adjust assistance to reduce the fatigue progression. Two experiments validate the EMG-free fatigue estimation method and the integrated MFAC, demonstrating accurate muscle activation estimation an