Frequency based EMG power spectrum analysis of Salat associated muscle contraction

2015 International Conference on Electrical & Electronic Engineering (ICEEE) Pub Date : 2015-11-01 DOI:10.1109/CEEE.2015.7428245

Farzana Khanam, Mohiudding Ahmad

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引用次数: 15

Abstract

Mean frequency (MNF) based EMG power spectrum analysis is presented to determine Salat associated muscle fatigue and indices. The main complexity of the parameter is a non-linear relationship between muscle fatigue and feature value, especially in large muscle and in cyclic dynamic contraction which is solved by this proposal. By this work, we can compute frequency dependent MNF for dynamic contractions and relaxations. Through Acknowledge software, FB-MNF is calculated and compared with the standard MNF. The results demonstrate that mean parameter of selected FB-MNF has a better linear relationship with muscle contraction compared to the others for different subjects. In addition, it has been observed through analysis of variance (ANOVA), compared to the traditional methods and have a significant difference (p<;0.05) between feature values among the MNF signal data of EMG power spectrum for different subjects. Furthermore, we have computed Mean Power (MNP), Total Power (TTP) and Peak Frequency (PKF) to determine both muscle load and muscle fatigue indices.

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Salat相关肌肉收缩的频率肌电功率谱分析

提出了基于平均频率(MNF)的肌电功率谱分析来确定Salat相关的肌肉疲劳及其指标。参数的复杂性主要是肌肉疲劳与特征值之间的非线性关系，特别是在大肌肉和循环动力收缩中，该方法解决了这一问题。通过这项工作，我们可以计算动态收缩和弛豫的频率相关MNF。通过Acknowledge软件计算FB-MNF，并与标准MNF进行比较。结果表明，在不同的实验对象中，所选择的FB-MNF平均参数与肌肉收缩的线性关系较好。此外，通过方差分析(ANOVA)观察到，与传统方法相比，不同受试者肌电功率谱的MNF信号数据的特征值之间存在显著差异(p<;0.05)。此外，我们计算了平均功率(MNP)，总功率(TTP)和峰值频率(PKF)来确定肌肉负荷和肌肉疲劳指标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 International Conference on Electrical & Electronic Engineering (ICEEE)

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