Muscle Synergy-Based Iterative Learning Control for Upper Limb Functional Electrical Stimulation in Stroke Rehabilitation

Abstract

Functional Electrical Stimulation (FES) is widely used in the postoperative rehabilitation of stroke patients. Multi-channel FES enables alternating stimulation of multiple muscle groups, effectively delaying muscle fatigue and facilitating precise control of complex upper limb movements. However, high-dimensional control of multiple muscles introduces additional challenges, particularly in coordinating antagonistic muscles and achieving efficient control. This study proposes a novel FES control framework that integrates muscle synergy theory, Long Short-Term Memory (LSTM) networks, and Iterative Learning Control (ILC). In this framework, the LSTM network predicts synergy activation coefficients from joint kinematics (angle and angular velocity), while the ILC algorithm iteratively updates electrical stimulation intensities based on the tracking error from previous iterations. This combination reduces the dimensionality of muscle control and improves the balance of muscle group activation, aligning better with natural motor control strategies. Experiments conducted on eight healthy subjects demonstrated that the proposed synergy-based ILC method significantly reduced joint angle tracking errors (measured by RMSE) over 10 stimulation iterations, compared to reference trajectories derived from voluntary motion. Specifically, in the combined elbow-wrist drinking task, the wrist RMSE decreased from 13.10° to 4.19°, and the elbow RMSE decreased from 45.07° to 5.53°. The coefficient of determination ( ${R}^{{2}}$ ), reflecting the goodness of fit between predicted and reference trajectories, exceeded 0.96, indicating high tracking accuracy and stability. Preliminary experiments on three stroke patients further support the adaptability and clinical potential of the proposed method.