AI-Driven Personalization of Functional Electrical Stimulation for Improved Paralysis Treatment Outcomes

Functional Electrical Stimulation (FES) is a prevalent method in neurorehabilitation aimed at restoring motor function in patients with paralysis. Traditional FES techniques frequently depend on fixed stimulation patterns, lacking responsiveness to specific patient reactions. This paper presents an AI-driven system utilizing Reinforcement Learning (RL) to personalize FES settings, hence enhancing rehabilitation outcomes dynamically. The method employs a model-free reinforcement learning agent designed to optimize stimulation intensity, timing, and muscle group targeting using real-time data, including limb trajectory, movement smoothness, and muscle fatigue indications. A reward function is formulated to equilibrate movement precision with the reduction of muscle fatigue. In comparison to traditional FES and rule-based adaptive techniques, the proposed RL-FES system demonstrated a 28.5% enhancement in movement accuracy, a 22.4% decrease in fatigue onset, and notable adaptability among patients with differing levels of disability. The findings illustrate the viability of clever closed-loop control systems in customizing neurostimulation treatment. This study emphasizes the possibility of integrating artificial intelligence with rehabilitation robots to develop more effective, personalized, and patient-centered neurorehabilitation procedures.