Abstract
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.
Abstract (translated)
功能电刺激(FES)越来越与其他康复设备,包括机器人,集成在一起。 FES 循环是康复中常见的 FES 应用之一,通过刺激腿部肌肉的一种特定模式来实现。适合该模式的 Pattern 因人而异,并且需要手动调整,这对于个人用户来说可能会很耗时且具有挑战性。在这里,我们提出了一种基于 AI 的方法来找到 Pattern,这个方法不需要额外的硬件或传感器。我们的方法有两个阶段,第一阶段是通过 reinforcement learning 和详细的肌肉骨骼模型来找到模型模式。这些模型是通过开源软件构建的,可以通过我们的自动化脚本进行定制,因此可以由非技术人员使用而无需额外成本。接下来,我们的方法和真实的循环数据一起微调模式。我们在仿真和实验中测试了这些方法,在一个静止的三轮车上进行了测试。在仿真测试中,我们的方法和不同的循环配置都能 robustly 输出模型模式。实验评估表明,我们的方法可以找到一个模型模式,其促进的循环速度比肌电学模式更高。仅使用循环数据中的 100 秒,我们的方法就可以输出一个微调的模式,提供更好的循环性能。除了 FES 循环,这项工作是一个展示,展示了人类参与 Loop AI 在真实康复中的可行性和潜力。
URL
https://arxiv.org/abs/2303.09986
