Development of a Functional Electrical Stimulation Device for Finger Flexion Control with Adaptive Electrode Selection using Reinforcement Learning

Abstract

This paper explores Functional Electrical Stimulation (FES) for finger movement control in rehabilitation, introducing a Reinforcement Learning-based automated electrode selection system, optimizing the search of stimulation points, addressing user variability. This research contributes to the long-term use of personalized and efficient FES systems for rehabilitation.