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）正日益与机器人等其他康复设备融合。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，且需要手动调整，这对个体用户而言既耗时又具挑战性。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。该方法分为两个阶段：首先利用强化学习和详细肌肉骨骼模型获取基于模型的模式。这些模型基于开源软件构建，可通过自动化脚本定制，因此无技术背景的用户无需额外成本即可使用。随后，该方法利用实际骑行数据对模式进行微调。我们在仿真和实验中对方法进行了验证，实验平台为固定式三轮车。仿真测试表明，该方法能针对不同骑行配置稳定生成基于模型的模式。实验评估显示，与基于肌电的模式相比，本方法生成的基于模型的模式可诱导更高的骑行速度。仅需100秒骑行数据，该方法即可输出微调后的模式，实现更优的骑行性能。本研究不仅限于FES骑行，更展示了人在回路的AI技术在现实康复中的可行性与潜力。