Electromyograph (EMG) signal analysis to predict muscle fatigue during driving

Electromyography (EMG) signal obtained from muscles need advance methods for detection, processing and classification. The purpose of this paper is to analyze muscle fatigue from EMG signals. At beginning, 15 subjects will an-swer a set of questionnaires. The score of the questionnaires will be calc...

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Main Authors:	Muhammad Amzar Syazani, Mohd Azli, Mahfuzah, Mustafa, Rafiuddin, Abdubrani, Amran, Abdul Hadi, S. N., Aqida, Zarith Liyana, Zahari
Format:	Book Section
Language:	English English English
Published:	Springer Singapore 2018
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/22861/ http://umpir.ump.edu.my/id/eprint/22861/ http://umpir.ump.edu.my/id/eprint/22861/ http://umpir.ump.edu.my/id/eprint/22861/1/35.%20Electromyograph%20%28EMG%29%20signal%20analysis%20to%20predict%20muscle%20fatigue%20during%20driving.pdf http://umpir.ump.edu.my/id/eprint/22861/13/52.%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf http://umpir.ump.edu.my/id/eprint/22861/14/52.1%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf

id	ump-22861
recordtype	eprints
spelling	ump-228612019-05-30T07:04:22Z http://umpir.ump.edu.my/id/eprint/22861/ Electromyograph (EMG) signal analysis to predict muscle fatigue during driving Muhammad Amzar Syazani, Mohd Azli Mahfuzah, Mustafa Rafiuddin, Abdubrani Amran, Abdul Hadi S. N., Aqida Zarith Liyana, Zahari TK Electrical engineering. Electronics Nuclear engineering Electromyography (EMG) signal obtained from muscles need advance methods for detection, processing and classification. The purpose of this paper is to analyze muscle fatigue from EMG signals. At beginning, 15 subjects will an-swer a set of questionnaires. The score of the questionnaires will be calculated and the score will determine if the driver is fatigue or mild fatigue or fatigue based on their driving habit. Next, EMG signals will be collected by placing two surface electrodes on the Brachioradialis muscle located at the forearm while driving Need For Speed (NFS) game. A simulation set of steering and pedals will be controlled during the driving game. The drivers drive for two hours and the EMG signal will be collected during they are driving. The output signals will be pre-process to remove any noise in the signal. After that, the data is normalized between value 0 to 1 and the signal is analyzed using frequency analysis and time analysis. Mean and variance will be calculated for time domain analysis and graph of mean vs variance is plotted. In frequency domain analysis, Power Spec-tral Density (PSD) is extracted from the peak frequency of PSD in each signal is obtained. All result will be divided into three classes: non-fatigue, mild-fatigue and fatigue. Based on result obtained in time domain, average normalized mean (non-fatigue: 0.5004), (mild-fatigue: 0.497) and (fatigue: 0.494). While, for fre-quency domain analysis, average peak frequency (non-fatigue: 13.379Hz), (mild-fatigue: 11.969Hz) and (fatigue: 12.782Hz). Springer Singapore 2018-08 Book Section PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22861/1/35.%20Electromyograph%20%28EMG%29%20signal%20analysis%20to%20predict%20muscle%20fatigue%20during%20driving.pdf pdf en http://umpir.ump.edu.my/id/eprint/22861/13/52.%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf pdf en http://umpir.ump.edu.my/id/eprint/22861/14/52.1%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf Muhammad Amzar Syazani, Mohd Azli and Mahfuzah, Mustafa and Rafiuddin, Abdubrani and Amran, Abdul Hadi and S. N., Aqida and Zarith Liyana, Zahari (2018) Electromyograph (EMG) signal analysis to predict muscle fatigue during driving. In: Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018. Lecture Notes in Electrical Engineering . Springer Singapore, Singapore, pp. 405-420. ISBN 978-981-13-3708-6 https://link.springer.com/chapter/10.1007/978-981-13-3708-6_35 DOI: https://doi.org/10.1007/978-981-13-3708-6_35
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Muhammad Amzar Syazani, Mohd Azli Mahfuzah, Mustafa Rafiuddin, Abdubrani Amran, Abdul Hadi S. N., Aqida Zarith Liyana, Zahari Electromyograph (EMG) signal analysis to predict muscle fatigue during driving
description	Electromyography (EMG) signal obtained from muscles need advance methods for detection, processing and classification. The purpose of this paper is to analyze muscle fatigue from EMG signals. At beginning, 15 subjects will an-swer a set of questionnaires. The score of the questionnaires will be calculated and the score will determine if the driver is fatigue or mild fatigue or fatigue based on their driving habit. Next, EMG signals will be collected by placing two surface electrodes on the Brachioradialis muscle located at the forearm while driving Need For Speed (NFS) game. A simulation set of steering and pedals will be controlled during the driving game. The drivers drive for two hours and the EMG signal will be collected during they are driving. The output signals will be pre-process to remove any noise in the signal. After that, the data is normalized between value 0 to 1 and the signal is analyzed using frequency analysis and time analysis. Mean and variance will be calculated for time domain analysis and graph of mean vs variance is plotted. In frequency domain analysis, Power Spec-tral Density (PSD) is extracted from the peak frequency of PSD in each signal is obtained. All result will be divided into three classes: non-fatigue, mild-fatigue and fatigue. Based on result obtained in time domain, average normalized mean (non-fatigue: 0.5004), (mild-fatigue: 0.497) and (fatigue: 0.494). While, for fre-quency domain analysis, average peak frequency (non-fatigue: 13.379Hz), (mild-fatigue: 11.969Hz) and (fatigue: 12.782Hz).
format	Book Section
author	Muhammad Amzar Syazani, Mohd Azli Mahfuzah, Mustafa Rafiuddin, Abdubrani Amran, Abdul Hadi S. N., Aqida Zarith Liyana, Zahari
author_facet	Muhammad Amzar Syazani, Mohd Azli Mahfuzah, Mustafa Rafiuddin, Abdubrani Amran, Abdul Hadi S. N., Aqida Zarith Liyana, Zahari
author_sort	Muhammad Amzar Syazani, Mohd Azli
title	Electromyograph (EMG) signal analysis to predict muscle fatigue during driving
title_short	Electromyograph (EMG) signal analysis to predict muscle fatigue during driving
title_full	Electromyograph (EMG) signal analysis to predict muscle fatigue during driving
title_fullStr	Electromyograph (EMG) signal analysis to predict muscle fatigue during driving
title_full_unstemmed	Electromyograph (EMG) signal analysis to predict muscle fatigue during driving
title_sort	electromyograph (emg) signal analysis to predict muscle fatigue during driving
publisher	Springer Singapore
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22861/ http://umpir.ump.edu.my/id/eprint/22861/ http://umpir.ump.edu.my/id/eprint/22861/ http://umpir.ump.edu.my/id/eprint/22861/1/35.%20Electromyograph%20%28EMG%29%20signal%20analysis%20to%20predict%20muscle%20fatigue%20during%20driving.pdf http://umpir.ump.edu.my/id/eprint/22861/13/52.%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf http://umpir.ump.edu.my/id/eprint/22861/14/52.1%20Electromyograph%20%28EMG%29%20Signal%20Analysis%20to%20Predict%20Muscle%20Fatigue%20During%20Driving.pdf
first_indexed	2023-09-18T22:33:59Z
last_indexed	2023-09-18T22:33:59Z
_version_	1777416472305336320

Electromyograph (EMG) signal analysis to predict muscle fatigue during driving

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