Surface electromyography (SEMG) normalization method based on pre fatigue maximal voluntary contraction

Surface electromyography (sEMG) pattern recognition task requires high accuracy classification. However, current technology suffers from two main problems. The first problem is inconsistent pattern due to fatigue while the second is robustness of sEMG features due to low signal to noise ratio, SNR....

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Main Author:	Mohd Sabri, Muhammad Ihsan
Format:	Thesis
Language:	English English
Published:	2017
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/20616/1/Surface%20Electromyography%20%28SEMG%29%20Normalization%20Method%20Based%20On%20Pre%20Fatigue%20Maximal%20Voluntary%20Contraction.pdf http://eprints.utem.edu.my/id/eprint/20616/2/Surface%20electromyography%20%28SEMG%29%20normalization%20method%20based%20on%20pre%20fatigue%20maximal%20voluntary%20contraction.pdf
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spelling	my-utem-ep.206162022-06-13T12:20:29Z Surface electromyography (SEMG) normalization method based on pre fatigue maximal voluntary contraction 2017 Mohd Sabri, Muhammad Ihsan T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Surface electromyography (sEMG) pattern recognition task requires high accuracy classification. However, current technology suffers from two main problems. The first problem is inconsistent pattern due to fatigue while the second is robustness of sEMG features due to low signal to noise ratio, SNR. This research intends to address both sEMG problems mentioned by proposing a normalization method named as pre fatigue maximal voluntary contraction (PFMVC) and a feature known as Maximal Amplitude Spectrum (MaxPS). The both method used to carry the objectives, the first is to analyse a normalization method based on pre fatigue maximal voluntary contraction (PFMVC) and the second objective is to design and verify a new features known as Maximal Amplitude Spectrum (MaxPS). It is found that the proposed method improves sEMG pattern recognition accuracy by 98.48% when compare to 97%. The performance of PFMVC normalization method is measured by mean variance of boxplot across several subject which is reduce inconsistency from 3.41x10-3 to 1.73x10-3 , p-value of one way analysis of variance (One- Way ANOVA) is reduce from p=0.25 to p=0.035 and variance of mean intra class correlation co-efficient, (ICC) is reduce from 26x10-4 to 7.089x10-4. The precision and robust of MaxPS features is determine by lowest Error to mean percentage (%ETM) which is 0.213 , lowest in Euclidean distance,(Ed) which is 0.0034 and lowest hoteling t2 which is 0.27. From the results, it shows that the MaxPS is a robust and precise feature for force and fatigue indicator. This will give the benefit for force and fatigue mapping application. 2017 Thesis http://eprints.utem.edu.my/id/eprint/20616/ http://eprints.utem.edu.my/id/eprint/20616/1/Surface%20Electromyography%20%28SEMG%29%20Normalization%20Method%20Based%20On%20Pre%20Fatigue%20Maximal%20Voluntary%20Contraction.pdf text en public http://eprints.utem.edu.my/id/eprint/20616/2/Surface%20electromyography%20%28SEMG%29%20normalization%20method%20based%20on%20pre%20fatigue%20maximal%20voluntary%20contraction.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=106115&query_desc=kw%2Cwrdl%3A%20Surface%20electromyography mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Electrical Engineering Miskon, Muhammad Fahmi
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Miskon, Muhammad Fahmi
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Mohd Sabri, Muhammad Ihsan Surface electromyography (SEMG) normalization method based on pre fatigue maximal voluntary contraction
description	Surface electromyography (sEMG) pattern recognition task requires high accuracy classification. However, current technology suffers from two main problems. The first problem is inconsistent pattern due to fatigue while the second is robustness of sEMG features due to low signal to noise ratio, SNR. This research intends to address both sEMG problems mentioned by proposing a normalization method named as pre fatigue maximal voluntary contraction (PFMVC) and a feature known as Maximal Amplitude Spectrum (MaxPS). The both method used to carry the objectives, the first is to analyse a normalization method based on pre fatigue maximal voluntary contraction (PFMVC) and the second objective is to design and verify a new features known as Maximal Amplitude Spectrum (MaxPS). It is found that the proposed method improves sEMG pattern recognition accuracy by 98.48% when compare to 97%. The performance of PFMVC normalization method is measured by mean variance of boxplot across several subject which is reduce inconsistency from 3.41x10-3 to 1.73x10-3 , p-value of one way analysis of variance (One- Way ANOVA) is reduce from p=0.25 to p=0.035 and variance of mean intra class correlation co-efficient, (ICC) is reduce from 26x10-4 to 7.089x10-4. The precision and robust of MaxPS features is determine by lowest Error to mean percentage (%ETM) which is 0.213 , lowest in Euclidean distance,(Ed) which is 0.0034 and lowest hoteling t2 which is 0.27. From the results, it shows that the MaxPS is a robust and precise feature for force and fatigue indicator. This will give the benefit for force and fatigue mapping application.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohd Sabri, Muhammad Ihsan
author_facet	Mohd Sabri, Muhammad Ihsan
author_sort	Mohd Sabri, Muhammad Ihsan
title	Surface electromyography (SEMG) normalization method based on pre fatigue maximal voluntary contraction
title_short	Surface electromyography (SEMG) normalization method based on pre fatigue maximal voluntary contraction
title_full	Surface electromyography (SEMG) normalization method based on pre fatigue maximal voluntary contraction
title_fullStr	Surface electromyography (SEMG) normalization method based on pre fatigue maximal voluntary contraction
title_full_unstemmed	Surface electromyography (SEMG) normalization method based on pre fatigue maximal voluntary contraction
title_sort	surface electromyography (semg) normalization method based on pre fatigue maximal voluntary contraction
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Electrical Engineering
publishDate	2017
url	http://eprints.utem.edu.my/id/eprint/20616/1/Surface%20Electromyography%20%28SEMG%29%20Normalization%20Method%20Based%20On%20Pre%20Fatigue%20Maximal%20Voluntary%20Contraction.pdf http://eprints.utem.edu.my/id/eprint/20616/2/Surface%20electromyography%20%28SEMG%29%20normalization%20method%20based%20on%20pre%20fatigue%20maximal%20voluntary%20contraction.pdf
_version_	1747833986666725376

Surface electromyography (SEMG) normalization method based on pre fatigue maximal voluntary contraction

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