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利用肌肉协同作用和小波变换来识别深蹲过程中的疲劳。

Use of muscle synergies and wavelet transforms to identify fatigue during squatting.

作者信息

Smale Kenneth B, Shourijeh Mohammad S, Benoit Daniel L

机构信息

Faculty of Health Sciences, University of Ottawa, Ottawa, Canada.

Faculty of Health Sciences, University of Ottawa, Ottawa, Canada; Faculty of Engineering, University of Ottawa, Ottawa, Canada.

出版信息

J Electromyogr Kinesiol. 2016 Jun;28:158-66. doi: 10.1016/j.jelekin.2016.04.008. Epub 2016 Apr 27.

DOI:10.1016/j.jelekin.2016.04.008
PMID:27156237
Abstract

The objective of this study was to supplement continuous wavelet transforms with muscle synergies in a fatigue analysis to better describe the combination of decreased firing frequency and altered activation profiles during dynamic muscle contractions. Nine healthy young individuals completed the dynamic tasks before and after they squatted with a standard Olympic bar until complete exhaustion. Electromyography (EMG) profiles were analyzed with a novel concatenated non-negative matrix factorization method that decomposed EMG signals into muscle synergies. Muscle synergy analysis provides the activation pattern of the muscles while continuous wavelet transforms output the temporal frequency content of the EMG signals. Synergy analysis revealed subtle changes in two-legged squatting after fatigue while differences in one-legged squatting were more pronounced and included the shift from a general co-activation of muscles in the pre-fatigue state to a knee extensor dominant weighting post-fatigue. Continuous wavelet transforms showed major frequency content decreases in two-legged squatting after fatigue while very few frequency changes occurred in one-legged squatting. It was observed that the combination of methods is an effective way of describing muscle fatigue and that muscle activation patterns play a very important role in maintaining the overall joint kinetics after fatigue.

摘要

本研究的目的是在疲劳分析中,将连续小波变换与肌肉协同作用相结合,以更好地描述动态肌肉收缩过程中放电频率降低和激活模式改变的组合情况。九名健康的年轻个体在使用标准奥林匹克杠铃进行深蹲直至完全疲劳前后,完成了动态任务。采用一种新颖的串联非负矩阵分解方法分析肌电图(EMG)图谱,该方法将EMG信号分解为肌肉协同作用。肌肉协同作用分析提供肌肉的激活模式,而连续小波变换输出EMG信号的时间频率内容。协同作用分析揭示了疲劳后双腿深蹲的细微变化,而单腿深蹲的差异更为明显,包括从疲劳前状态下肌肉的一般共同激活转变为疲劳后以膝关节伸肌为主的权重。连续小波变换显示,疲劳后双腿深蹲的主要频率成分降低,而单腿深蹲中很少发生频率变化。研究发现,方法的结合是描述肌肉疲劳的有效方式,并且肌肉激活模式在疲劳后维持整体关节动力学方面起着非常重要的作用。

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