Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion
Studying muscle fatigue plays an important role in preventing the risks associated with musculoskeletal disorders. The effect of elbow-joint angle on time-frequency parameters during a repetitive motion provides valuable information in finding the most accurate position of the angle causing muscle f...
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doaj-013b1b5fa1274f449ae7c127e6237b352021-10-11T08:03:06ZengVSB-Technical University of OstravaAdvances in Electrical and Electronic Engineering1336-13761804-31192017-01-0115342443410.15598/aeee.v15i3.2173915Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint MotionTriwiyanto Triwiyanto0Oyas Wahyunggoro1Hanung Adi Nugroho2Herianto Herianto3Department of Electrical Engineering & Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Grafika No. 2, Yogyakarta, Indonesia & Department of Electromedical Engineering, Politeknik Kesehatan Surabaya, Pucang Jajar Timur No. 10, Surabaya, IndonesiaDepartment of Electrical Engineering & Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Grafika No. 2, Yogyakarta, IndonesiaDepartment of Electrical Engineering & Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Grafika No. 2, Yogyakarta, IndonesiaDepartment of Electromedical Engineering, Politeknik Kesehatan Surabaya, Pucang Jajar Timur No. 10, Surabaya, IndonesiaStudying muscle fatigue plays an important role in preventing the risks associated with musculoskeletal disorders. The effect of elbow-joint angle on time-frequency parameters during a repetitive motion provides valuable information in finding the most accurate position of the angle causing muscle fatigue. Therefore, the purpose of this study is to analyze the effect of muscle fatigue on the spectral and time-frequency domain parameters derived from electromyography (EMG) signals using the Continuous Wavelet Transform (CWT). Four male participants were recruited to perform a repetitive motion (flexion and extension movements) from a non-fatigue to fatigue condition. EMG signals were recorded from the biceps muscle. The recorded EMG signals were then analyzed offline using the complex Morlet wavelet. The time-frequency domain data were analyzed using the time-averaged wavelet spectrum (TAWS) and the Scale-Average Wavelet Power (SAWP) parameters. The spectral domain data were analyzed using the Instantaneous Mean Frequency (IMNF) and the Instantaneous Mean Power Spectrum (IMNP) parameters. The index of muscle fatigue was observed by calculating the increase of the IMNP and the decrease of the IMNF parameters. After performing a repetitive motion from non-fatigue to fatigue condition, the average of the IMNF value decreased by 15.69% and the average of the IMNP values increased by 84%, respectively. This study suggests that the reliable frequency band to detect muscle fatigue is 31.10-36.19Hz with linear regression parameters of 0.979mV^2Hz^(-1) and 0.0095mV^2Hz^(-1) for R^2 and slope, respectively.http://advances.utc.sk/index.php/AEEE/article/view/2173cwtelbow joint angleemgmuscle fatiguewavelet. |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Triwiyanto Triwiyanto Oyas Wahyunggoro Hanung Adi Nugroho Herianto Herianto |
spellingShingle |
Triwiyanto Triwiyanto Oyas Wahyunggoro Hanung Adi Nugroho Herianto Herianto Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion Advances in Electrical and Electronic Engineering cwt elbow joint angle emg muscle fatigue wavelet. |
author_facet |
Triwiyanto Triwiyanto Oyas Wahyunggoro Hanung Adi Nugroho Herianto Herianto |
author_sort |
Triwiyanto Triwiyanto |
title |
Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion |
title_short |
Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion |
title_full |
Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion |
title_fullStr |
Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion |
title_full_unstemmed |
Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion |
title_sort |
continuous wavelet transform analysis of surface electromyography for muscle fatigue assessment on the elbow joint motion |
publisher |
VSB-Technical University of Ostrava |
series |
Advances in Electrical and Electronic Engineering |
issn |
1336-1376 1804-3119 |
publishDate |
2017-01-01 |
description |
Studying muscle fatigue plays an important role in preventing the risks associated with musculoskeletal disorders. The effect of elbow-joint angle on time-frequency parameters during a repetitive motion provides valuable information in finding the most accurate position of the angle causing muscle fatigue. Therefore, the purpose of this study is to analyze the effect of muscle fatigue on the spectral and time-frequency domain parameters derived from electromyography (EMG) signals using the Continuous Wavelet Transform (CWT). Four male participants were recruited to perform a repetitive motion (flexion and extension movements) from a non-fatigue to fatigue condition. EMG signals were recorded from the biceps muscle. The recorded EMG signals were then analyzed offline using the complex Morlet wavelet. The time-frequency domain data were analyzed using the time-averaged wavelet spectrum (TAWS) and the Scale-Average Wavelet Power (SAWP) parameters. The spectral domain data were analyzed using the Instantaneous Mean Frequency (IMNF) and the Instantaneous Mean Power Spectrum (IMNP) parameters. The index of muscle fatigue was observed by calculating the increase of the IMNP and the decrease of the IMNF parameters. After performing a repetitive motion from non-fatigue to fatigue condition, the average of the IMNF value decreased by 15.69% and the average of the IMNP values increased by 84%, respectively. This study suggests that the reliable frequency band to detect muscle fatigue is 31.10-36.19Hz with linear regression parameters of 0.979mV^2Hz^(-1) and 0.0095mV^2Hz^(-1) for R^2 and slope, respectively. |
topic |
cwt elbow joint angle emg muscle fatigue wavelet. |
url |
http://advances.utc.sk/index.php/AEEE/article/view/2173 |
work_keys_str_mv |
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