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Functional electrical stimulation (FES) has been increasingly integrated withother rehabilitation devices, including robots. FES cycling is one of thecommon FES applications in rehabilitation, which is performed by stimulatingleg muscles in a certain pattern. The appropriate pattern varies acrossindividuals and requires manual tuning which can be time-consuming andchallenging for the individual user. Here, we present an AI-based method forfinding the patterns, which requires no extra hardware or sensors. Our methodhas two phases, starting with finding model-based patterns using reinforcementlearning and detailed musculoskeletal models. The models, built usingopen-source software, can be customised through our automated script and can betherefore used by non-technical individuals without extra cost. Next, ourmethod fine-tunes the pattern using real cycling data. We test our both insimulation and experimentally on a stationary tricycle. In the simulation test,our method can robustly deliver model-based patterns for different cyclingconfigurations. The experimental evaluation shows that our method can find amodel-based pattern that induces higher cycling speed than an EMG-basedpattern. By using just 100 seconds of cycling data, our method can deliver afine-tuned pattern that gives better cycling performance. Beyond FES cycling,this work is a showcase, displaying the feasibility and potential ofhuman-in-the-loop AI in real-world rehabilitation.

Towards AI-controlled FES-restoration of movements: Learning cycling stimulation pattern with reinforcement learning