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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.12026 |
| _version_ | 1798000302640594944 |
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| author | Lakshmi M. Hari G. Venugopal S. Ramakrishnan |
| author_facet | Lakshmi M. Hari G. Venugopal S. Ramakrishnan |
| author_sort | Lakshmi M. Hari |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T11:18:04Z |
| format | Article |
| id | doaj.art-27f0a8268526460197c714fc8ae9a924 |
| institution | Directory Open Access Journal |
| issn | 0013-5194 1350-911X |
| language | English |
| last_indexed | 2024-04-11T11:18:04Z |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj.art-27f0a8268526460197c714fc8ae9a9242022-12-22T04:27:09ZengWileyElectronics Letters0013-51941350-911X2021-01-01571424410.1049/ell2.12026Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decompositionLakshmi M. Hari0G. Venugopal1S. Ramakrishnan2Biomedical Group, Department of Applied Mechanics Indian Institute of Technology Madras Chennai IndiaNSS College of Engineering Palakkad APJ Abdul Kalam Technology University Kerala IndiaBiomedical Group, Department of Applied Mechanics Indian Institute of Technology Madras Chennai IndiaAbstract 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.https://doi.org/10.1049/ell2.12026Electrical activity in neurophysiological processesSignal processing and detectionBioelectric signalsDigital signal processingIntegral transformsOther topics in statistics |
| spellingShingle | Lakshmi M. Hari G. Venugopal S. Ramakrishnan Muscle fatigue analysis in biceps brachii surface electromyography signals using synchrosqueezed Morlet wavelet and singular value decomposition Electronics Letters Electrical activity in neurophysiological processes Signal processing and detection Bioelectric signals Digital signal processing Integral transforms Other topics in statistics |
| title | 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_short | 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 |
| topic | Electrical activity in neurophysiological processes Signal processing and detection Bioelectric signals Digital signal processing Integral transforms Other topics in statistics |
| url | https://doi.org/10.1049/ell2.12026 |
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