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）正越来越多地与机器人等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。合适的刺激模式因人而异，且需要手动调节，这对个体用户而言既耗时又具挑战性。本文提出了一种基于AI的模式寻优方法，无需额外硬件或传感器。该方法包含两个阶段：首先利用强化学习和详细肌肉骨骼模型生成基于模型的模式。这些模型通过开源软件构建，可通过自动化脚本定制，因此非技术用户可零成本使用。其次，方法利用真实骑行数据对模式进行微调。我们通过仿真和实验在固定式三轮车上验证了该方法。仿真测试表明，该方法能针对不同骑行配置鲁棒地生成基于模型的模式。实验评估显示，该方法找到的基于模型的模式可比基于肌电信号的模式诱导更高骑行速度。仅需100秒骑行数据，该方法即可输出微调模式并实现更优骑行性能。除FES骑行外，本研究亦展示了人机协同AI在真实康复场景中的可行性与潜力。