Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition

Abstract Muscle fatigue during isometric contraction of biceps brachii is analysed using synchrosqueezed continuous wavelet transform with Morlet wavelet and singular value decomposition (SVD) features. The recorded surface electromyography signals are decomposed to time frequency matrix using Morle...

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Autores principales:	Lakshmi M. Hari, G. Venugopal, S. Ramakrishnan
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/27f0a8268526460197c714fc8ae9a924
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spelling	oai:doaj.org-article:27f0a8268526460197c714fc8ae9a9242021-11-16T10:18:23ZMuscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition1350-911X0013-519410.1049/ell2.12026https://doaj.org/article/27f0a8268526460197c714fc8ae9a9242021-01-01T00:00:00Zhttps://doi.org/10.1049/ell2.12026https://doaj.org/toc/0013-5194https://doaj.org/toc/1350-911XAbstract Muscle fatigue during isometric contraction of biceps brachii is analysed using synchrosqueezed continuous wavelet transform with Morlet wavelet and singular value decomposition (SVD) features. The recorded surface electromyography signals are decomposed to time frequency matrix using Morlet wavelet and the characteristics are extracted using singular value features such as maximum singular value and zero crossing frequency. The percentage difference in feature values for each segment with the progression of fatigue is calculated. Results show that the recorded signals are complex, non‐stationary, multicomponent, and random in nature. Maximum singular value represents the non‐stationarity of a signal, with an increasing trend towards the fatigue condition. Zero crossing frequency represents the complexity or randomness in the signals and it decreases with the progression of fatigue. It is found that both the features are statistically significant with p < 0.01. It appears that the synchrosqueezed continuous wavelet transform and singular value features are able to analyse fatigue in surface electromyography signals.Lakshmi M. HariG. VenugopalS. RamakrishnanWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElectronics Letters, Vol 57, Iss 1, Pp 42-44 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 Lakshmi M. Hari G. Venugopal S. Ramakrishnan Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition
description	Abstract Muscle fatigue during isometric contraction of biceps brachii is analysed using synchrosqueezed continuous wavelet transform with Morlet wavelet and singular value decomposition (SVD) features. The recorded surface electromyography signals are decomposed to time frequency matrix using Morlet wavelet and the characteristics are extracted using singular value features such as maximum singular value and zero crossing frequency. The percentage difference in feature values for each segment with the progression of fatigue is calculated. Results show that the recorded signals are complex, non‐stationary, multicomponent, and random in nature. Maximum singular value represents the non‐stationarity of a signal, with an increasing trend towards the fatigue condition. Zero crossing frequency represents the complexity or randomness in the signals and it decreases with the progression of fatigue. It is found that both the features are statistically significant with p < 0.01. It appears that the synchrosqueezed continuous wavelet transform and singular value features are able to analyse fatigue in surface electromyography signals.
format	article
author	Lakshmi M. Hari G. Venugopal S. Ramakrishnan
author_facet	Lakshmi M. Hari G. Venugopal S. Ramakrishnan
author_sort	Lakshmi M. Hari
title	Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition
title_short	Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition
title_full	Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition
title_fullStr	Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition
title_full_unstemmed	Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition
title_sort	muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed morlet wavelet and singular value decomposition
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/27f0a8268526460197c714fc8ae9a924
work_keys_str_mv	AT lakshmimhari musclefatigueanalysisinbicepsbrachiisurfaceelectromyographysignalsusingsynchrosqueezedmorletwaveletandsingularvaluedecomposition AT gvenugopal musclefatigueanalysisinbicepsbrachiisurfaceelectromyographysignalsusingsynchrosqueezedmorletwaveletandsingularvaluedecomposition AT sramakrishnan musclefatigueanalysisinbicepsbrachiisurfaceelectromyographysignalsusingsynchrosqueezedmorletwaveletandsingularvaluedecomposition
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Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition

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