利用同步挤压小波变换和奇异值特征分析肱二头肌的动态收缩和疲劳。
Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features.
机构信息
Non-Invasive Imaging and Diagnostic Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
Department of Instrumentation and Control Engineering, NSS College of Engineering, Palakkad, APJ Abdul Kalam Technology University, Kerala, India.
出版信息
Proc Inst Mech Eng H. 2022 Feb;236(2):208-217. doi: 10.1177/09544119211048011. Epub 2021 Oct 11.
In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.
在这项研究中，使用同步挤压小波变换（SST）和表面肌电图（sEMG）信号的奇异值特征分析肱二头肌的动态收缩和相关的疲劳状态。为此，使用 SST 将记录的信号分解为时频矩阵。分析中使用了两种解析函数，即 Morlet 和 Bump 小波。奇异值分解方法应用于该时频矩阵，以得出最大奇异值（MSV）、奇异值熵（SVEn）和奇异值能量（SVEr）等特征。结果表明，这两种小波都能够在动态疲劳收缩期间对 sEMG 信号中的非平稳变化进行特征化。随着疲劳的发展，MSV 和 SVEr 值的增加表示 sEMG 信号中存在非平稳性。随着疲劳的发展，SVEn 值的降低表明信号的随机性。因此，似乎可以采用这种方法来描述不同神经肌肉条件下的动态肌肉收缩。
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