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Functional Electrical Stimulation (FES) can restore motion to a paralysedperson's muscles. Yet, control stimulating many muscles to restore thepractical function of entire limbs is an unsolved problem. Currentneurostimulation engineering still relies on 20th Century control approachesand correspondingly shows only modest results that require daily tinkering tooperate at all. Here, we present our state of the art Deep ReinforcementLearning (RL) developed for real time adaptive neurostimulation of paralysedlegs for FES cycling. Core to our approach is the integration of a personalisedneuromechanical component into our reinforcement learning framework that allowsus to train the model efficiently without demanding extended training sessionswith the patient and working out of the box. Our neuromechanical componentincludes merges musculoskeletal models of muscle and or tendon function and amultistate model of muscle fatigue, to render the neurostimulation responsiveto a paraplegic's cyclist instantaneous muscle capacity. Our RL approachoutperforms PID and Fuzzy Logic controllers in accuracy and performance.Crucially, our system learned to stimulate a cyclist's legs from ramping upspeed at the start to maintaining a high cadence in steady state racing as themuscles fatigue. A part of our RL neurostimulation system has been successfullydeployed at the Cybathlon 2020 bionic Olympics in the FES discipline with ourparaplegic cyclist winning the Silver medal among 9 competing teams.

Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling