IEEE Trans Neural Syst Rehabil Eng. 2022;30:2613-2622. doi: 10.1109/TNSRE.2022.3203970. Epub 2022 Sep 19.
Functional electrical stimulation (FES) can be used to restore motor function to people with paralysis caused by spinal cord injuries (SCIs). However, chronically-paralyzed FES-stimulated muscles can fatigue quickly, which may decrease FES controller performance. In this work, we explored the feasibility of using deep neural network (DNN) controllers trained with reinforcement learning (RL) to control FES of upper-limb muscles after SCI. We developed upper-limb biomechanical models that exhibited increased muscle fatigability, decreased muscle recovery, and decreased muscle strength, as observed in people with chronic SCIs. Simulations confirmed that controller training time and controller performance are impaired to varying degrees by muscle fatigability. Also, the simulations showed that large muscle strength asymmetries between opposing muscles can substantially impair controller performance. However, the results of this study suggest that controller performance for highly-fatigable musculoskeletal systems can be preserved by allowing for rest between movements. Overall, the results suggest that RL can be used to successfully train FES controllers, even for highly-fatigable musculoskeletal systems.
功能性电刺激 (FES) 可用于恢复因脊髓损伤 (SCI) 导致瘫痪的人的运动功能。然而，长期瘫痪的 FES 刺激肌肉会很快疲劳，这可能会降低 FES 控制器的性能。在这项工作中，我们探索了使用基于强化学习 (RL) 训练的深度神经网络 (DNN) 控制器来控制 SCI 后上肢肌肉 FES 的可行性。我们开发了上肢生物力学模型，这些模型表现出肌肉疲劳性增加、肌肉恢复能力下降和肌肉力量下降，这些现象在慢性 SCI 患者中都有观察到。模拟结果证实，肌肉疲劳性在不同程度上会影响控制器的训练时间和性能。此外，模拟结果表明，拮抗肌之间的肌肉强度明显不对称会极大地损害控制器的性能。然而，这项研究的结果表明，通过允许在运动之间休息，可以保持对高度疲劳的肌肉骨骼系统的控制器性能。总的来说，结果表明，即使是对高度疲劳的肌肉骨骼系统，RL 也可以成功地用于训练 FES 控制器。
