适应性调整FES配置文件使用规范-最佳代学习控制的脚下降纠正
概括
这项研究开发了一个自适应的功能电刺激 (FES) 控制器,使用动态模型和正常-最佳代学习控制 (NOILC) 进行中风患者的自然脚下降校正.
科学领域:
- 生物医学工程 生物医学工程
- 康复机器人 康复机器人
- 神经修复品是一种神经修复品.
背景情况:
- 功能电刺激 (FES) 是脚下降纠正的关键辅助技术.
- 当前的FES控制器往往缺乏适应性,并产生不自然的肌肉激活.
- 动态步态变化给现有的FES系统带来了挑战.
研究的目的:
- 为自然和适应性刺激开发一个优化的FES配置文件.
- 为了补偿中风患者的步态障碍.
- 为了改善行走过程中脚的背部屈曲.
主要方法:
- 开发了一个汉默斯坦结构的脚关节动态模型,将FES配置文件与肌肉骨反应联系起来.
- 设计了一个基于标准最佳代学习控制 (NOILC) 的FES控制器.
- 优化控制学习收益以实现自动轨迹错误校正.
主要成果:
- 拟议的控制器实现了脚背部屈曲,使脚靠近参考轨迹.
- 在摇摆阶段的最大背部屈曲率提高了3.92° (vs.没有FES) 和2.06° (vs.固定轮FES).
- 在五名中风患者中证明了增强的步行表现.
结论:
- 新的FES控制器提供了自然和适应性刺激配置文件.
- 这种方法显著提高了中风幸存者的步行表现.
- 显示出在落脚康复中临床应用的强大潜力.
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