Pilot Study Of Electromyography Analysis Of The Arm Muscle Using Levenberg-Marquadt Back Propagation Neural Network

The study on the EMG signal is useful in providing the information regarding to the force and motion command that can be used in clinical research, rehabilitation and assistive technology. However, it is difficult for one feature parameter to reflect as a unique feature of the measured EMG signals t...

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Main Author:	Yahya, Abu Bakar
Format:	Thesis
Language:	English English
Published:	2017
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/22412/1/Pilot%20Study%20Of%20Electromyography%20Analysis%20Of%20The%20Arm%20Muscle%20Using%20Levenberg-Marquadt%20Back%20Propagation%20Neural%20Network%20-%20Abu%20Bakar%20Yahya%20-%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/22412/2/Pilot%20Study%20Of%20Electromyography%20Analysis%20Of%20The%20Arm%20Muscle%20Using%20Levenberg-Marquadt%20Back%20Propagation%20Neural%20Network.pdf
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institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Chong, Shin Horng
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Yahya, Abu Bakar Pilot Study Of Electromyography Analysis Of The Arm Muscle Using Levenberg-Marquadt Back Propagation Neural Network
description	The study on the EMG signal is useful in providing the information regarding to the force and motion command that can be used in clinical research, rehabilitation and assistive technology. However, it is difficult for one feature parameter to reflect as a unique feature of the measured EMG signals to the force and motion commands perfectly. Moreover, it is challenging to identify and classify the muscle force that exerted by a muscle and the muscle activities according to a specific movement. This research aims to propose EMG signal pattern recognition that based on back propagation neural network approach to identify the force and motion commands. This research focuses on the upper arm muscle, which is the biceps brachii muscle that leads in the improvement of the system of the prosthetic upper arm. The proposed EMG signal pattern recognition is consisting of data acquisition, data processing, data classification and data testing. The data acquisition phase is designed to acquire EMG signal from the subject. Features extraction for the EMG signal is carried out in the data processing phase. In this phase, statistical features such as maximum amplitude, mean and root mean square are computed for the features extraction purpose. In data classification phase, the three extracted time domain features are used as inputs to train the measured EMG signal via Levenberg-Marquadt backpropagation neural network training function. Then the EMG signal is classified via conjugate gradient backpropagation neural network training function. In data testing phases, three additional subjects are selected to follow the proposed EMG signal pattern recognition phases. In this research, muscle force model is used to determine the value of the force exerted by the biceps muscle. The muscle force model is based on the lever system in human body, which is third class lever. The results from the statistical analysis shows that the changes of the amplitude of the EMG signal are changing correlated to the changes of the muscle force exerted by the biceps muscle depending on the size of the loads. The analysis of pattern recognition for the measured EMG signal shows a good performance of the classification. The EMG signal can be classified based on the tasks of different weight of loads and different angle of the arm motion. The analysis of the muscle force model shows that the value of the muscle force exerted by the biceps muscle is different for all subjects. As the conclusion, it is proved that this research has been successfully accomplished and the relevance of the relationship between the changes in the movement of the hand towards the EMG signal changes and the changes of the force exerted by the biceps muscle has been proved. These findings are useful to be applied on the development of the assistive technology in helping the disabled person. These findings also can lead to improve the system of the assistive technology, especially for the improvement of prosthetic arms.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Yahya, Abu Bakar
author_facet	Yahya, Abu Bakar
author_sort	Yahya, Abu Bakar
title	Pilot Study Of Electromyography Analysis Of The Arm Muscle Using Levenberg-Marquadt Back Propagation Neural Network
title_short	Pilot Study Of Electromyography Analysis Of The Arm Muscle Using Levenberg-Marquadt Back Propagation Neural Network
title_full	Pilot Study Of Electromyography Analysis Of The Arm Muscle Using Levenberg-Marquadt Back Propagation Neural Network
title_fullStr	Pilot Study Of Electromyography Analysis Of The Arm Muscle Using Levenberg-Marquadt Back Propagation Neural Network
title_full_unstemmed	Pilot Study Of Electromyography Analysis Of The Arm Muscle Using Levenberg-Marquadt Back Propagation Neural Network
title_sort	pilot study of electromyography analysis of the arm muscle using levenberg-marquadt back propagation neural network
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electrical Engineering
publishDate	2017
url	http://eprints.utem.edu.my/id/eprint/22412/1/Pilot%20Study%20Of%20Electromyography%20Analysis%20Of%20The%20Arm%20Muscle%20Using%20Levenberg-Marquadt%20Back%20Propagation%20Neural%20Network%20-%20Abu%20Bakar%20Yahya%20-%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/22412/2/Pilot%20Study%20Of%20Electromyography%20Analysis%20Of%20The%20Arm%20Muscle%20Using%20Levenberg-Marquadt%20Back%20Propagation%20Neural%20Network.pdf
_version_	1747834020068065280
spelling	my-utem-ep.224122022-03-15T10:46:50Z Pilot Study Of Electromyography Analysis Of The Arm Muscle Using Levenberg-Marquadt Back Propagation Neural Network 2017 Yahya, Abu Bakar T Technology (General) TK Electrical engineering. Electronics Nuclear engineering The study on the EMG signal is useful in providing the information regarding to the force and motion command that can be used in clinical research, rehabilitation and assistive technology. However, it is difficult for one feature parameter to reflect as a unique feature of the measured EMG signals to the force and motion commands perfectly. Moreover, it is challenging to identify and classify the muscle force that exerted by a muscle and the muscle activities according to a specific movement. This research aims to propose EMG signal pattern recognition that based on back propagation neural network approach to identify the force and motion commands. This research focuses on the upper arm muscle, which is the biceps brachii muscle that leads in the improvement of the system of the prosthetic upper arm. The proposed EMG signal pattern recognition is consisting of data acquisition, data processing, data classification and data testing. The data acquisition phase is designed to acquire EMG signal from the subject. Features extraction for the EMG signal is carried out in the data processing phase. In this phase, statistical features such as maximum amplitude, mean and root mean square are computed for the features extraction purpose. In data classification phase, the three extracted time domain features are used as inputs to train the measured EMG signal via Levenberg-Marquadt backpropagation neural network training function. Then the EMG signal is classified via conjugate gradient backpropagation neural network training function. In data testing phases, three additional subjects are selected to follow the proposed EMG signal pattern recognition phases. In this research, muscle force model is used to determine the value of the force exerted by the biceps muscle. The muscle force model is based on the lever system in human body, which is third class lever. The results from the statistical analysis shows that the changes of the amplitude of the EMG signal are changing correlated to the changes of the muscle force exerted by the biceps muscle depending on the size of the loads. The analysis of pattern recognition for the measured EMG signal shows a good performance of the classification. The EMG signal can be classified based on the tasks of different weight of loads and different angle of the arm motion. The analysis of the muscle force model shows that the value of the muscle force exerted by the biceps muscle is different for all subjects. As the conclusion, it is proved that this research has been successfully accomplished and the relevance of the relationship between the changes in the movement of the hand towards the EMG signal changes and the changes of the force exerted by the biceps muscle has been proved. These findings are useful to be applied on the development of the assistive technology in helping the disabled person. These findings also can lead to improve the system of the assistive technology, especially for the improvement of prosthetic arms. 2017 Thesis http://eprints.utem.edu.my/id/eprint/22412/ http://eprints.utem.edu.my/id/eprint/22412/1/Pilot%20Study%20Of%20Electromyography%20Analysis%20Of%20The%20Arm%20Muscle%20Using%20Levenberg-Marquadt%20Back%20Propagation%20Neural%20Network%20-%20Abu%20Bakar%20Yahya%20-%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/22412/2/Pilot%20Study%20Of%20Electromyography%20Analysis%20Of%20The%20Arm%20Muscle%20Using%20Levenberg-Marquadt%20Back%20Propagation%20Neural%20Network.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=107365 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electrical Engineering Chong, Shin Horng 1. Ahmad, S.A., 2009. Moving Approximate Entropy and its Application to the Electromyographic Control of an Artificial Hand. University of Southamptom, pp.36. 2. 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