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Reaching disabilities affect the quality of life. Functional ElectricalStimulation (FES) can restore lost motor functions. Yet, there remainchallenges in controlling FES to induce desired movements. Neuromechanicalmodels are valuable tools for developing FES control methods. However, focusingon the upper extremity areas, several existing models are either overlysimplified or too computationally demanding for control purposes. Besides themodel-related issues, finding a general method for governing the control rulesfor different tasks and subjects remains an engineering challenge. Here, wepresent our approach toward FES-based restoration of arm movements to addressthose fundamental issues in controlling FES. Firstly, we present oursurface-FES-oriented neuromechanical models of human arms built usingwell-accepted, open-source software. The models are designed to capturesignificant dynamics in FES controls with minimal computational cost. Ourmodels are customisable and can be used for testing different control methods.Secondly, we present the application of reinforcement learning (RL) as ageneral method for governing the control rules. In combination, ourcustomisable models and RL-based control method open the possibility ofdelivering customised FES controls for different subjects and settings withminimal engineering intervention. We demonstrate our approach in planar and 3Dsettings.

Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching