Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction

Muscle fatigue is defined as a reduction in muscle’s ability to contract and produce force due to prolonged submaximal exercise. Since fatigue is not a physical variable, fatigue indices are commonly used to detect and monitor muscle fatigue development. One suggested approach to quantitative measur...

Full description

Saved in:

Bibliographic Details
Main Author:	Kamaruddin, Nurul Asyikin
Format:	Thesis
Language:	English
Published:	2016
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/77841/1/NurulAsyikinKamaruddinMFKE2016.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.77841
record_format	uketd_dc
spelling	my-utm-ep.778412018-07-04T11:46:11Z Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction 2016-02 Kamaruddin, Nurul Asyikin TK Electrical engineering. Electronics Nuclear engineering Muscle fatigue is defined as a reduction in muscle’s ability to contract and produce force due to prolonged submaximal exercise. Since fatigue is not a physical variable, fatigue indices are commonly used to detect and monitor muscle fatigue development. One suggested approach to quantitative measurement of muscle fatigue is based on surface electromyography (sEMG) signal. Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) are commonly used techniques to obtain time-frequency representation of sEMG signals. However, S Transform (ST) technique has not been applied much to physiological signals. No found literature has used ST technique to extract muscle fatigue indices. Thus, this study intends to determine the feasibility of using ST technique to extract muscle fatigue indices from sEMG signal. Thirty college students with no illness history were randomly selected to perform bicep curl activities for 130 seconds while holding a 2 kg dumbbell. Using the three time-frequency techniques (STFT, CWT, and ST), four commonly extracted muscle fatigue indices (Instantaneous Energy Distribution (IED), Instantaneous Mean Frequency (IMNF), Instantaneous Frequency Variance (IFV) and Instantaneous Normalize Spectral Moment (INSM)) were extracted from the acquired biceps sEMG signals. Indices from fatigue signals were found to be significantly different (p-value < 0.05) from the non-fatigue signals. Based on the Normalization of Root Mean Square Error (NRMSE) and Relative Error, ST technique was found to produce less error than STFT and CWT techniques in extracting muscle fatigue indices. Through the use of 3-fold cross validation procedure and with the help of Support Vector Machine (SVM) classifier, IMNF-IED-IFV was selected as the best feature combination for classifying the two phases of muscle fatigue with consistent classification performance (accuracy, sensitivity and specificity) of 80%. Therefore, this study concludes that ST processing technique is feasible to be applied to sEMG signals for extracting screening or monitoring measures of muscle fatigue with a good degree of certainty. 2016-02 Thesis http://eprints.utm.my/id/eprint/77841/ http://eprints.utm.my/id/eprint/77841/1/NurulAsyikinKamaruddinMFKE2016.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:94075 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Kamaruddin, Nurul Asyikin Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction
description	Muscle fatigue is defined as a reduction in muscle’s ability to contract and produce force due to prolonged submaximal exercise. Since fatigue is not a physical variable, fatigue indices are commonly used to detect and monitor muscle fatigue development. One suggested approach to quantitative measurement of muscle fatigue is based on surface electromyography (sEMG) signal. Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) are commonly used techniques to obtain time-frequency representation of sEMG signals. However, S Transform (ST) technique has not been applied much to physiological signals. No found literature has used ST technique to extract muscle fatigue indices. Thus, this study intends to determine the feasibility of using ST technique to extract muscle fatigue indices from sEMG signal. Thirty college students with no illness history were randomly selected to perform bicep curl activities for 130 seconds while holding a 2 kg dumbbell. Using the three time-frequency techniques (STFT, CWT, and ST), four commonly extracted muscle fatigue indices (Instantaneous Energy Distribution (IED), Instantaneous Mean Frequency (IMNF), Instantaneous Frequency Variance (IFV) and Instantaneous Normalize Spectral Moment (INSM)) were extracted from the acquired biceps sEMG signals. Indices from fatigue signals were found to be significantly different (p-value < 0.05) from the non-fatigue signals. Based on the Normalization of Root Mean Square Error (NRMSE) and Relative Error, ST technique was found to produce less error than STFT and CWT techniques in extracting muscle fatigue indices. Through the use of 3-fold cross validation procedure and with the help of Support Vector Machine (SVM) classifier, IMNF-IED-IFV was selected as the best feature combination for classifying the two phases of muscle fatigue with consistent classification performance (accuracy, sensitivity and specificity) of 80%. Therefore, this study concludes that ST processing technique is feasible to be applied to sEMG signals for extracting screening or monitoring measures of muscle fatigue with a good degree of certainty.
format	Thesis
qualification_level	Master's degree
author	Kamaruddin, Nurul Asyikin
author_facet	Kamaruddin, Nurul Asyikin
author_sort	Kamaruddin, Nurul Asyikin
title	Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction
title_short	Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction
title_full	Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction
title_fullStr	Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction
title_full_unstemmed	Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction
title_sort	temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2016
url	http://eprints.utm.my/id/eprint/77841/1/NurulAsyikinKamaruddinMFKE2016.pdf
_version_	1747817844138049536

Temporal spectral approach to surface electromyography based fatigue classification of biceps brachii during dynamic contraction

Similar Items