Electromyography (EMG) Signal Analysis For Manual lifting Using Time-Frequency Distribution

In manufacturing industries, manual lifting is commonly practiced by workers in their routine to move or transport objects to a desired place. Manual lifting with higher repetition and loading using biceps muscle contribute to the effects of soft tissues and muscle fatigue that affect the performanc...

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Main Author:	Tengku Zawawi, Tengku Nor Shuhada
Format:	Thesis
Language:	English English
Published:	2016
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/18172/1/Electromyography%20%28EMG%29%20Signal%20Analysis%20For%20Manual%20lifting%20Using%20Time-Frequency%20Distribution%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18172/2/Electromyography%20%28EMG%29%20Signal%20Analysis%20Of%20Manual%20Lifting%20Using%20Time-Frequency%20Distribution.pdf
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id	my-utem-ep.18172
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Abdullah, Abdul Rahim
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Tengku Zawawi, Tengku Nor Shuhada Electromyography (EMG) Signal Analysis For Manual lifting Using Time-Frequency Distribution
description	In manufacturing industries, manual lifting is commonly practiced by workers in their routine to move or transport objects to a desired place. Manual lifting with higher repetition and loading using biceps muscle contribute to the effects of soft tissues and muscle fatigue that affect the performance and efficiency of the worker. Electromyography (EMG) is a device to detect the signal’s muscle that is use to investigate muscular disorder. Fast-Fourier transform is the common technique used in signal processing. However, this technique only present spectral information and have the limitation to provide the time-frequency information. EMG signals is complicated and highly complex which is consists of variable frequency and amplitude. Thus, time-frequency analysis technique is needed to be employed to provide spectral and temporal information of the signal. This research presents the analysis of EMG signal using Fast-Fourier Transform and time-frequency distribution (TFD) which is spectrogram to estimate the parameters. Manual lifting activities is repeated to five times with the different load mass and lifting height are performed until achieve muscle fatigue to collect the data. From experiments, the raw data of EMG signals were collected via Measurement Configuration Data Collection of NORAXON INC. The parameters are extracted from EMG signal such as instantaneous root mean square (RMS) voltage, mean of RMS voltage and instantaneous energy to determine the information of manual lifting behaviour such as muscle fatigue, strength and energy transfer for the subject’s performance evaluation. The results show the relationship between all the parameters involve in manual lifting activities and its behaviour. The higher subjects is easier to handle manual lifting with the higher lifting height, but tough body have advantage to handle higher load mass. The increasing of load masses and lifting height are highly proportional to the strength and energy transfer, however inversely proportional to reach muscle fatigue. The overall results conclude that, the application of spectrogram clearly give the information of the subject’s muscle performance based on the manual lifting activities.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Tengku Zawawi, Tengku Nor Shuhada
author_facet	Tengku Zawawi, Tengku Nor Shuhada
author_sort	Tengku Zawawi, Tengku Nor Shuhada
title	Electromyography (EMG) Signal Analysis For Manual lifting Using Time-Frequency Distribution
title_short	Electromyography (EMG) Signal Analysis For Manual lifting Using Time-Frequency Distribution
title_full	Electromyography (EMG) Signal Analysis For Manual lifting Using Time-Frequency Distribution
title_fullStr	Electromyography (EMG) Signal Analysis For Manual lifting Using Time-Frequency Distribution
title_full_unstemmed	Electromyography (EMG) Signal Analysis For Manual lifting Using Time-Frequency Distribution
title_sort	electromyography (emg) signal analysis for manual lifting using time-frequency distribution
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Electrical Engineering
publishDate	2016
url	http://eprints.utem.edu.my/id/eprint/18172/1/Electromyography%20%28EMG%29%20Signal%20Analysis%20For%20Manual%20lifting%20Using%20Time-Frequency%20Distribution%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18172/2/Electromyography%20%28EMG%29%20Signal%20Analysis%20Of%20Manual%20Lifting%20Using%20Time-Frequency%20Distribution.pdf
_version_	1747833913861996544
spelling	my-utem-ep.181722021-10-10T14:53:25Z Electromyography (EMG) Signal Analysis For Manual lifting Using Time-Frequency Distribution 2016 Tengku Zawawi, Tengku Nor Shuhada T Technology (General) TK Electrical engineering. Electronics Nuclear engineering In manufacturing industries, manual lifting is commonly practiced by workers in their routine to move or transport objects to a desired place. Manual lifting with higher repetition and loading using biceps muscle contribute to the effects of soft tissues and muscle fatigue that affect the performance and efficiency of the worker. Electromyography (EMG) is a device to detect the signal’s muscle that is use to investigate muscular disorder. Fast-Fourier transform is the common technique used in signal processing. However, this technique only present spectral information and have the limitation to provide the time-frequency information. EMG signals is complicated and highly complex which is consists of variable frequency and amplitude. Thus, time-frequency analysis technique is needed to be employed to provide spectral and temporal information of the signal. This research presents the analysis of EMG signal using Fast-Fourier Transform and time-frequency distribution (TFD) which is spectrogram to estimate the parameters. Manual lifting activities is repeated to five times with the different load mass and lifting height are performed until achieve muscle fatigue to collect the data. From experiments, the raw data of EMG signals were collected via Measurement Configuration Data Collection of NORAXON INC. The parameters are extracted from EMG signal such as instantaneous root mean square (RMS) voltage, mean of RMS voltage and instantaneous energy to determine the information of manual lifting behaviour such as muscle fatigue, strength and energy transfer for the subject’s performance evaluation. The results show the relationship between all the parameters involve in manual lifting activities and its behaviour. The higher subjects is easier to handle manual lifting with the higher lifting height, but tough body have advantage to handle higher load mass. The increasing of load masses and lifting height are highly proportional to the strength and energy transfer, however inversely proportional to reach muscle fatigue. The overall results conclude that, the application of spectrogram clearly give the information of the subject’s muscle performance based on the manual lifting activities. 2016 Thesis http://eprints.utem.edu.my/id/eprint/18172/ http://eprints.utem.edu.my/id/eprint/18172/1/Electromyography%20%28EMG%29%20Signal%20Analysis%20For%20Manual%20lifting%20Using%20Time-Frequency%20Distribution%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/18172/2/Electromyography%20%28EMG%29%20Signal%20Analysis%20Of%20Manual%20Lifting%20Using%20Time-Frequency%20Distribution.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=99998 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Electrical Engineering Abdullah, Abdul Rahim 1. Abdullah, A. R., Norddin, N., Abidin, N. Q. Z., Aman, A. & Jopri, M. H., 2012. 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Electromyography (EMG) Signal Analysis For Manual lifting Using Time-Frequency Distribution

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