  Reaching disabilities affect the quality of life. Functional Electrical
Stimulation (FES) can restore lost motor functions. Yet, there remain
challenges in controlling FES to induce desired movements. Neuromechanical
models are valuable tools for developing FES control methods. However, focusing
on the upper extremity areas, several existing models are either overly
simplified or too computationally demanding for control purposes. Besides the
model-related issues, finding a general method for governing the control rules
for different tasks and subjects remains an engineering challenge. Here, we
present our approach toward FES-based restoration of arm movements to address
those fundamental issues in controlling FES. Firstly, we present our
surface-FES-oriented neuromechanical models of human arms built using
well-accepted, open-source software. The models are designed to capture
significant dynamics in FES controls with minimal computational cost. Our
models are customisable and can be used for testing different control methods.
Secondly, we present the application of reinforcement learning (RL) as a
general method for governing the control rules. In combination, our
customisable models and RL-based control method open the possibility of
delivering customised FES controls for different subjects and settings with
minimal engineering intervention. We demonstrate our approach in planar and 3D
settings.
