Analysis of the Muscle Fatigue Based on Band Spectrum Entropy of Multi-channel Surface Electromyography_Journal of Biomedical Engineering

Authors：

LIUJian ¹ ,  ZOURenling ¹ , ZHANGDongheng ² , XUXiulin ¹ , HUXiufang ¹

1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Shanghai University of Medicine and Health Sciences, Shanghai 200093, China;

Corresponding author：

ZOURenling, Email: zourenling@163.com

Keywords：

surface electromyography; fatigue index; multi-channel; band spectrum entropy

DOI：

10.7507/1001-5515.20160073

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Exercise-induced muscle fatigue is a phenomenon that the maximum voluntary contraction force or power output of muscle is temporarily reduced due to muscular movement. If the fatigue is not treated properly, it will bring about a severe injury to the human body. With multi-channel collection of lower limb surface electromyography signals, this article analyzes the muscle fatigue by adoption of band spectrum entropy method which combined electromyographic signal spectral analysis and nonlinear dynamics. The experimental result indicated that with the increase of muscle fatigue, muscle signal spectrum began to move to low frequency, the energy concentrated, the system complexity came down, and the band spectrum entropy which reflected the complexity was also reduced. By monitoring the entropy, we can measure the degree of muscle fatigue, and provide an indicator to judge fatigue degree for the sports training and clinical rehabilitation training.

Citation： LIUJian, ZOURenling, ZHANGDongheng, XUXiulin, HUXiufang. Analysis of the Muscle Fatigue Based on Band Spectrum Entropy of Multi-channel Surface Electromyography. Journal of Biomedical Engineering, 2016, 33(3): 431-435. doi: 10.7507/1001-5515.20160073 Copy

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1. BIGLAND-RITCHIE B, WOODS J J. Changes in muscle contractile properties and neural control during human muscular fatigue[J]. Muscle Nerve, 1985, 7(9) :691-699.
2. 王前,曹霞,尹冠军,等.超声图像熵特性的肌肉疲劳进程评估[J].中国生物医学工程学报,2015,34(1) :30-36.
3. PAISS O, INBAR G F. Autoregressive modeling of surface EMG and its spectrum with application to fatigue[J]. IEEE Trans Biomed Eng, 1987, 34(10) :761-770.
4. 丁海曙.人体运动信息检测与处理[M].北京:宇航出版社,1992:94-128.
5. MERLETTI R, LO CONTE L, AVIGNONE E, et al. Modeling of surface myoelectric signals——Part Ⅰ:Model implementation[J]. IEEE Trans Biomed Eng, 1999, 46(7) :810-820.
6. 刘加海,王丽,王健.基于相空间、熵和复杂度变化的表面肌电信号分析[J].浙江大学学报(理学版),2006,33(2) :182-186.
7. 陈伟婷.基于熵的表面肌电信号特征提取研究[D].上海:上海交通大学,2008.

Journal of Biomedical Engineering

Analysis of the Muscle Fatigue Based on Band Spectrum Entropy of Multi-channel Surface Electromyography

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