表面电动图的概率密度函数及其依赖于巨大的侧面体中的收缩力
Javier Rodriguez-Falces1, Armando Malanda2, Cristina Mariscal3
1Department of Electrical and Electronical Engineering, Public University of Navarra D.I.E.E., Campus de Arrosadía S/N, 31006, Pamplona, Spain. javier.rodriguez@unavarra.es.
Biomedical engineering online
|October 27, 2024
概括
逐渐增加的肌肉收缩力揭示了表面电动图 (sEMG) 概率密度函数 (PDF) 形状的变化. 这种方法与固定的收缩不同,在10%以上的MVC上显示了较低的个体变异性.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 生物力学 生物力学
背景情况:
- 已知表面电动图 (sEMG) 信号的概率密度函数 (PDF) 取决于肌肉收缩力.
- 之前的研究使用固定的收缩力水平 (例如,最大自愿收缩的百分比 - MVC) 调查了这种关系.
- 这种方法可能无法完全捕捉力变化时sEMG PDF形状的动态变化.
研究的目的:
- 在不断增加的收缩力期间,从零到最大自愿收缩 (MVC) 期间,研究sEMG PDF形状的演变.
- 将结果与使用离散力水平的传统方法进行比较.
- 为了确定sEMG PDF-force关系中的个体变异性如何随着收缩强度的增加而变化.
主要方法:
- 在健康的受试者中,从广侧面肌肉记录了表面EMG信号.
- 肌肉力量从零开始逐渐和持续地增加.
- 分析了sEMG PDF形状和EMG填充因子,使用纠正的sEMG信号的非中心时刻.
主要成果:
- 在84%的受试者中,sEMG PDF形状在半退化和高斯分布之间振荡,随着力从0增加到10%的MVC.
- 在PDF与力关系中的个体变异性在MVC10%以下的力中很高,但在这个值以上显著减少.
- 聚合分析显示,从半退化分布到拉普拉斯分布 (0-5% MVC) 的快速转变,随后在更高的力量下向高斯分布的过渡速度较慢.
结论:
- 从零开始的逐渐增强力对于可靠地评估sEMG PDF形状对收缩力的依赖至关重要.
- 动态评估方法为sEMG信号特征的非线性演变提供了洞察力.
- 在10%以上的MVC中,减少了个体间的变异性,这表明在较高的收缩水平下,神经控制策略更一致.
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