将重力纳入对上肢运动的协同控制中,使用与正负权重的相位协同作用
Alessandro Scano1, Cristina Brambilla1, Marta Russo2
1Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Advanced Methods for Biomedical Signal and Image Processing Laboratory, Italian Council of National Research (CNR), Milan, Italy.
Journal of applied physiology (Bethesda, Md. : 1985)
|June 6, 2025
概括
这项研究引入了一种新的肌肉协同模型,该模型结合了重力,揭示了重力补偿和尽量减少努力的策略在运动控制中并存. 这种新的框架增强了我们对身体如何在重力下控制运动的理解.
科学领域:
- 神经科学是一个神经科学.
- 生物力学 生物力学
- 发动机控制器的控制器
背景情况:
- 现有的肌肉协同模型往往忽视了重力对运动控制的影响.
- 两种模型,重力补偿和努力优化,提出了重力如何影响四肢运动的不同机制.
- 肌肉协同作用虽然有用,但缺乏对引力力的明确表示.
研究的目的:
- 通过提取负权重的协同效应,将重力整合到肌肉协同效应模型中.
- 为了捕获暗示重力利用的负电肌图 (EMG) 组件.
- 调查补偿和尽量减少努力的模型是否可以在统一的协同作用框架内共存.
主要方法:
- 应用混合矩阵因子化 (MMF) 来提取积极和负权重的肌肉协同作用.
- 分析了15名健康参与者在各种目标平面上的上肢达到运动.
- 根据重力利用的初始强度活动,将运动分为"增强强度EMG" (ITE) 和"减少强度EMG" (DTE).
主要成果:
- 与负重量的协同作用成功捕获了负相 EMG 组件,表明了重力利用.
- 与DTE相比,ITE运动显示出更好的重建准确度,协同效应较少.
- DTE运动表现出更多的负相激活和协同权重,这表明对重力利用的依赖更大.
- 引导式分析和集群分析揭示了ITE和DTE协同效应的不同结构.
结论:
- 开发的肌肉协同作用框架可以解释重力利用,调和补偿和尽量减少努力的模型.
- 这种新的方法提供了一个协同控制器,平衡了简单性,静态重力补偿和努力优化.
- 这些发现为在重力负荷下运动的神经控制提供了新的见解.
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