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)已越来越多地和其他康复设备(包括机器人)整合。 FES循环是康复中常见的FES应用程序,它通过刺激腿部肌肉以一定的模式进行。适当的模式因个体而异,需要手动调整,这可能耗时,且对个人用户具有挑战性。在这里,我们提出了一种基于人工智能的方法来寻找模式,该方法不需要额外的硬件或传感器。我们的方法有两个阶段,首先使用强化学习和详细的肌肉骨骼模型找到基于模型的模式。使用开源软件构建的模型可以通过我们的自动化脚本进行定制,因此可供非技术人员使用,无需额外费用。接下来,我们的方法使用实际骑行数据微调模式。我们在模拟和静止三轮车实验中测试我们的方法。在模拟测试中,我们的方法可以稳健地为不同的骑车配置提供基于模型的模式。实验评估显示,我们的方法可以找到诱导更高骑车速度的基于模型的模式,而比基于EMG的模式表现更好。仅使用100秒的骑车数据,我们的方法就可以提供更好的骑车性能的微调模式。这项工作超越了FES循环,是人与AI交互式控制技术在真实康复中的可行性和潜力的一个展示。