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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Main Authors: Triwiyanto Triwiyanto, Oyas Wahyunggoro, Hanung Adi Nugroho, Herianto Herianto
Format: Article
Language:English
Published: VSB-Technical University of Ostrava 2017-01-01
Series:Advances in Electrical and Electronic Engineering
Subjects:
cwt
emg
Online Access:http://advances.utc.sk/index.php/AEEE/article/view/2173
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spelling 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
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