下肢关节 扭矩 基于运动功能障碍患者的肌肉协同作用的连续估计模型
概括
这项研究介绍了一种新型模型,使用表面电肌图信号来估计下肢关节扭矩,用于外骨架机器人. 该模型准确预测部和膝盖扭矩,这对于患者康复至关重要.
科学领域:
- 生物医学工程 生物医学工程
- 机器人技术 机器人技术 机器人技术
- 神经科学是一个神经科学.
背景情况:
- 下肢关节扭矩估计对于有效的康复外骨控制至关重要.
- 表面电肌图 (sEMG) 为辅助技术提供了对人类运动的洞察力.
研究的目的:
- 开发一个神经控制水平模型,将肌电信号与下肢关节扭矩联系起来.
- 为了提高恢复机器人的扭矩估计的准确性和长期适应性.
主要方法:
- 从sEMG信号建立了一个肌肉生物电激活模型.
- 开发了一个运动神经激活模型,结合了肌肉协同作用.
- 集成的深度学习与自我注意力和对抗性转移学习用于连续扭矩估计.
主要成果:
- 该模型准确地估计了8名患有下肢运动功能障碍的患者的部和膝关节关节扭矩.
- 在长期使用中实现了高性能,决策系数为0.92 ± 0.06 (部) 和0.95 ± 0.03 (膝盖).
结论:
- 拟议的模型提供了一种可靠的方法,用于使用sEMG估计下肢关节扭矩.
- 这项研究为基于外骨架的康复中先进的按需辅助控制铺平了道路.
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