Classification of surface EMG signals for early signs of prolonged fatigue

Sports training are very important to athlete in maintaining and improving their performance. During training, adequate rest is essential to allow recuperation and build body strength. Inadequate rest may expose the body to prolonged fatigue (PF). This condition needs to be managed accordingly to av...

Full description

Saved in:

Bibliographic Details
Main Author:	Jamaluddin, Nurul Fauzani
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Electrodiagnosis Electromyography
Online Access:	http://psasir.upm.edu.my/id/eprint/68574/1/FK%202018%2032%20-%20IR.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-upm-ir.68574
record_format	uketd_dc
spelling	my-upm-ir.685742019-05-17T00:24:06Z Classification of surface EMG signals for early signs of prolonged fatigue 2017-10 Jamaluddin, Nurul Fauzani Sports training are very important to athlete in maintaining and improving their performance. During training, adequate rest is essential to allow recuperation and build body strength. Inadequate rest may expose the body to prolonged fatigue (PF). This condition needs to be managed accordingly to avoid chronic fatigue syndrome. Currently, the non-invasive assessment in identifying PF are training log record, questionnaire and Borg scale. Recent findings indicate that there are strong characteristics on surface electromyography (EMG) under PF conditions such as glycogen breakdown, existence of lactate and soreness. This study extends the investigation of PF signs, especially on the inceptions of PF. An experiment has been conducted on twenty participants to investigate the behavior of surface EMG during five days of intensive training that was based on Bruce Protocol treadmill test. The intention was to induce PF on biceps femoris (BF), rectus femoris (RF), vastus lateralis (VL) and vastus medialis (VM). Besides surface EMG signals, physiological measurements were also collected from the participant. Physiological results demonstrate that the earliest PF signs developed were soreness, lethargy and performance decrement. For the surface EMG, they went through three main processes: de-noising, feature extraction and classification. De-noising technique through stationary wavelet transform (SWT) was employed in enhancing quality of surface EMG signals. During de-noising process, new method in estimating threshold (Th) was proposed. The method demonstrated to have higher performance in term on noise removal and accuracy in PF classification, compared to conventional Th methods. Nine features extracted from the de-noised surface EMG signals. There were changes in median frequency (ΔFmed), mean frequency (ΔFmean), mean absolute value (ΔMAV), root mean square (ΔRMS), and five features from wavelet indices (ΔWI). Daily fatigue mappings indicate that the emergence of PF can be traced based on extracted features. The mappings indicate that ΔFmed and ΔFmean tend to increase under PF condition for all four muscles BF, RF, VL and VM. Additionally, under PF condition, the mapping indicates an increase in ΔRMS and ΔMAV but decrease in ΔWI for RF muscle. In the classification stage, Naïve Bayes (NB) and Support Vector Machine (SVM) demonstrate accuracy with 98% and 97% respectively, in distinguish PF on RF, 94% and 96% respectively on BF, both 95% on VL, and 98% and 96% respectively on VM. Thus, this study successfully demonstrates that surface EMG can be used in identifying the inception of PF. The findings presented are significant in sports field to prevent higher degree of PF. Electrodiagnosis Electromyography 2017-10 Thesis http://psasir.upm.edu.my/id/eprint/68574/ http://psasir.upm.edu.my/id/eprint/68574/1/FK%202018%2032%20-%20IR.pdf text en public doctoral Universiti Putra Malaysia Electrodiagnosis Electromyography
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Electrodiagnosis Electromyography
spellingShingle	Electrodiagnosis Electromyography Jamaluddin, Nurul Fauzani Classification of surface EMG signals for early signs of prolonged fatigue
description	Sports training are very important to athlete in maintaining and improving their performance. During training, adequate rest is essential to allow recuperation and build body strength. Inadequate rest may expose the body to prolonged fatigue (PF). This condition needs to be managed accordingly to avoid chronic fatigue syndrome. Currently, the non-invasive assessment in identifying PF are training log record, questionnaire and Borg scale. Recent findings indicate that there are strong characteristics on surface electromyography (EMG) under PF conditions such as glycogen breakdown, existence of lactate and soreness. This study extends the investigation of PF signs, especially on the inceptions of PF. An experiment has been conducted on twenty participants to investigate the behavior of surface EMG during five days of intensive training that was based on Bruce Protocol treadmill test. The intention was to induce PF on biceps femoris (BF), rectus femoris (RF), vastus lateralis (VL) and vastus medialis (VM). Besides surface EMG signals, physiological measurements were also collected from the participant. Physiological results demonstrate that the earliest PF signs developed were soreness, lethargy and performance decrement. For the surface EMG, they went through three main processes: de-noising, feature extraction and classification. De-noising technique through stationary wavelet transform (SWT) was employed in enhancing quality of surface EMG signals. During de-noising process, new method in estimating threshold (Th) was proposed. The method demonstrated to have higher performance in term on noise removal and accuracy in PF classification, compared to conventional Th methods. Nine features extracted from the de-noised surface EMG signals. There were changes in median frequency (ΔFmed), mean frequency (ΔFmean), mean absolute value (ΔMAV), root mean square (ΔRMS), and five features from wavelet indices (ΔWI). Daily fatigue mappings indicate that the emergence of PF can be traced based on extracted features. The mappings indicate that ΔFmed and ΔFmean tend to increase under PF condition for all four muscles BF, RF, VL and VM. Additionally, under PF condition, the mapping indicates an increase in ΔRMS and ΔMAV but decrease in ΔWI for RF muscle. In the classification stage, Naïve Bayes (NB) and Support Vector Machine (SVM) demonstrate accuracy with 98% and 97% respectively, in distinguish PF on RF, 94% and 96% respectively on BF, both 95% on VL, and 98% and 96% respectively on VM. Thus, this study successfully demonstrates that surface EMG can be used in identifying the inception of PF. The findings presented are significant in sports field to prevent higher degree of PF.
format	Thesis
qualification_level	Doctorate
author	Jamaluddin, Nurul Fauzani
author_facet	Jamaluddin, Nurul Fauzani
author_sort	Jamaluddin, Nurul Fauzani
title	Classification of surface EMG signals for early signs of prolonged fatigue
title_short	Classification of surface EMG signals for early signs of prolonged fatigue
title_full	Classification of surface EMG signals for early signs of prolonged fatigue
title_fullStr	Classification of surface EMG signals for early signs of prolonged fatigue
title_full_unstemmed	Classification of surface EMG signals for early signs of prolonged fatigue
title_sort	classification of surface emg signals for early signs of prolonged fatigue
granting_institution	Universiti Putra Malaysia
publishDate	2017
url	http://psasir.upm.edu.my/id/eprint/68574/1/FK%202018%2032%20-%20IR.pdf
_version_	1747812598102884352

Classification of surface EMG signals for early signs of prolonged fatigue

Similar Items