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

翻译：功能性电刺激（FES）正日益与包括机器人在内的其他康复设备相结合。FES骑行是康复中常见的FES应用之一，通过按特定模式刺激腿部肌肉实现。合适的模式因人而异，需要手动调节，这既耗时又对个体用户构成挑战。本文提出一种基于人工智能的模式发现方法，无需额外硬件或传感器。我们的方法分为两个阶段：首先利用强化学习和详细肌肉骨骼模型寻找基于模型的模式。这些模型采用开源软件构建，可通过自动化脚本定制，因此非技术背景用户也能无额外成本使用。随后，我们的方法利用真实骑行数据对模式进行微调。我们在仿真和实验中对固定式三轮车进行了测试。仿真测试表明，我们的方法能稳健地为不同骑行配置提供基于模型的模式。实验评估显示，与基于肌电信号的模式相比，我们的方法可找到能诱导更高骑行速度的基于模型模式。仅需100秒骑行数据，我们的方法就能提供经微调的模式，从而实现更优骑行性能。超越FES骑行本身，本研究作为示范案例，展示了人在回路中的人工智能在真实康复场景中的可行性与潜力。