面向AI控制的功能性电刺激运动恢复：基于强化学习的骑行刺激模式学习
Towards AI-controlled FES-restoration of movements: Learning cycling stimulation pattern with reinforcement learning
AI总结：
本文提出一种无需额外硬件的AI方法，通过强化学习和肌肉骨骼模型生成并微调FES骑行刺激模式，实验证明其性能优于EMG模式，展示了人在回路AI在康复中的潜力。
摘要：
功能性电刺激（FES）已越来越多地与其他康复设备（包括机器人）集成。FES骑行是康复中常见的FES应用之一，通过以特定模式刺激腿部肌肉来实现。合适的模式因人而异，需要手动调整，这对个体用户而言可能耗时且具有挑战性。在此，我们提出一种基于AI的方法来寻找这些模式，该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的肌肉骨骼模型来寻找基于模型的模式。这些模型使用开源软件构建，可通过我们的自动化脚本进行定制，因此非技术性个人无需额外成本即可使用。其次，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（在固定三轮车上）中均测试了该方法。在仿真测试中，我们的方法能够稳健地为不同骑行配置提供基于模型的模式。实验评估表明，我们的方法能够找到一种基于模型的模式，其诱导的骑行速度高于基于肌电（EMG）的模式。仅使用100秒的骑行数据，我们的方法就能提供一种微调后的模式，带来更好的骑行性能。超越FES骑行，这项工作是一个展示，证明了人在回路AI在现实世界康复中的可行性和潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
