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.

翻译：抓取障碍严重影响生活质量。功能性电刺激（FES）可恢复受损的运动功能，但控制FES以诱发预期动作仍面临挑战。神经力学模型是开发FES控制方法的重要工具，然而针对上肢区域的现有模型存在两种极端：要么过度简化，要么计算负荷过高以致无法满足控制需求。除模型问题外，如何为不同任务与受试者建立普适性控制规则仍然是工程难题。本文提出基于FES的手臂运动恢复方法，旨在解决FES控制中的根本问题。首先，我们基于公认的开源软件构建了面向表面电刺激的人手臂神经力学模型，该模型以最小计算成本捕捉FES控制中的关键动力学特征，具有可定制性且适用于不同控制方法的测试。其次，我们提出将强化学习（RL）作为通用方法制定控制规则。通过结合可定制模型与RL控制方法，我们实现了在最小化工程干预下为不同受试者与场景定制FES控制方案，并在二维平面与三维空间中验证了该方法的有效性。