在手腕运动中的肌肉协同作用基于非负的Tucker分解
Xiaoling Chen1,2, Yange Feng1, Qingya Chang1
1Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究使用非负的塔克分解 (NTD) 来分析手腕运动期间的肌肉协同作用. 在频率和运动领域,NTD有效地确定了共享和特定的肌肉协同作用,为运动控制提供了洞察力.
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:
- 肌肉协同作用简化了中枢神经系统的复杂运动.
- 了解频率和运动领域的肌肉协同作用对于运动控制研究至关重要.
研究的目的:
- 使用非负的塔克尔分解 (NTD) 探索频率和运动领域的肌肉协同作用.
- 在手腕曲和伸展过程中识别共享和特定的肌肉协同模块.
主要方法:
- 记录了手腕曲 (WF) 和手腕伸展 (WE) 过程中从10名健康受试者的8个上肢肌肉的表面电肌图 (sEMG).
- 应用非负的塔克分解 (NTD) 来分析来自sEMG数据的多域肌肉协同作用.
主要成果:
- 在WF期间识别了具有明显光谱组成部分的协同曲器对 (PL-FDS,ECR-FCR).
- 在WE期间确定了具有不同的光谱成分的协同扩展器对 (ED-ECU,ECR-B).
- 发现双臂 (BB) 作为一个共享模块,而其他曲器和延伸器分别针对WF和WE阶段.
结论:
- NTD是分析多通道sEMG信号中的多域协同特性的一种有价值的方法.
- 这些发现提高了对肌肉协同作用的频率特征和共享/特定模块的理解.
- 这项研究扩大了神经系统内运动控制的视角.
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