Biceps brachii surface EMG classification using neural networks

This thesis presents an approach of MATLAB-based system for clinical rehabilitation monitoring application. The main rationale for the development of such a system is that the pattern of the EMG signals elicited may differ depending on the activity of the muscle movement. Therefore, this research...

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Main Author:	Chong, Yee Lin
Format:	Thesis
Language:	English
Subjects:	EMG signals Biceps brachii muscle MATLAB-based system Human anatomy
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/20819/1/full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/20819/2/p.%201-24.pdf
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spelling	my-unimap-208192012-09-04T04:41:29Z Biceps brachii surface EMG classification using neural networks Chong, Yee Lin This thesis presents an approach of MATLAB-based system for clinical rehabilitation monitoring application. The main rationale for the development of such a system is that the pattern of the EMG signals elicited may differ depending on the activity of the muscle movement. Therefore, this research aims to study EMG signals elicited from biceps brachii muscle and classify the signal pattern to their respective class of activity. The proposed system consists of two main parts. The first part is about the development of an EMG acquisition platform. This platform consists of three modules; acquisition module, preprocessing module and feature extraction module. The acquisition module is used to acquire EMG signals from the subject. Several signal processing methods are carried out in the preprocessing module, where the EMG signal will undergo a series of processes like filtering, rectification and integration. After preprocessing, the signal is passed to the feature extraction module. In this module, statistical features such as mean, maximum, variance and standard deviation are computed to represent the signal pattern. The second part is regarding EMG pattern classification using neural networks. Feedforward BackPropagation Network (BPN) and Probabilistic Neural Network (PNN) are chosen as the classifiers to classify muscle activities. In the experimentation phase, 30 female subjects took part in this study. They were asked to perform several series of voluntary movement with respect to biceps brachii muscle. The experimental results show that EMG signals of different biceps activity is differed and simple statistical features are sufficient to represent the EMG pattern. The proposed BPN with Levenberg-Marquardt (LM) algorithm and PNN had achieved an overall classification rate of 88% while BPN with Resilient-Propagation (RP) algorithm achieved an overall classification of 87.11%. With these satisfactory results, the effectiveness of the proposed classifiers in EMG pattern classification problem is proven. Universiti Malaysia Perlis (UniMAP) 2009 Thesis en http://dspace.unimap.edu.my/123456789/20819 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/20819/1/full%20text.pdf ea14dadbaf7c55a3ab6136694e59af9e http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/20819/2/p.%201-24.pdf 3d51f7561f8c10cce80db60e1203c312 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/20819/3/license.txt b71c0ee6383ea6e985a34dfeb372e921 EMG signals Biceps brachii muscle MATLAB-based system Human anatomy School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	EMG signals Biceps brachii muscle MATLAB-based system Human anatomy
spellingShingle	EMG signals Biceps brachii muscle MATLAB-based system Human anatomy Chong, Yee Lin Biceps brachii surface EMG classification using neural networks
description	This thesis presents an approach of MATLAB-based system for clinical rehabilitation monitoring application. The main rationale for the development of such a system is that the pattern of the EMG signals elicited may differ depending on the activity of the muscle movement. Therefore, this research aims to study EMG signals elicited from biceps brachii muscle and classify the signal pattern to their respective class of activity. The proposed system consists of two main parts. The first part is about the development of an EMG acquisition platform. This platform consists of three modules; acquisition module, preprocessing module and feature extraction module. The acquisition module is used to acquire EMG signals from the subject. Several signal processing methods are carried out in the preprocessing module, where the EMG signal will undergo a series of processes like filtering, rectification and integration. After preprocessing, the signal is passed to the feature extraction module. In this module, statistical features such as mean, maximum, variance and standard deviation are computed to represent the signal pattern. The second part is regarding EMG pattern classification using neural networks. Feedforward BackPropagation Network (BPN) and Probabilistic Neural Network (PNN) are chosen as the classifiers to classify muscle activities. In the experimentation phase, 30 female subjects took part in this study. They were asked to perform several series of voluntary movement with respect to biceps brachii muscle. The experimental results show that EMG signals of different biceps activity is differed and simple statistical features are sufficient to represent the EMG pattern. The proposed BPN with Levenberg-Marquardt (LM) algorithm and PNN had achieved an overall classification rate of 88% while BPN with Resilient-Propagation (RP) algorithm achieved an overall classification of 87.11%. With these satisfactory results, the effectiveness of the proposed classifiers in EMG pattern classification problem is proven.
format	Thesis
author	Chong, Yee Lin
author_facet	Chong, Yee Lin
author_sort	Chong, Yee Lin
title	Biceps brachii surface EMG classification using neural networks
title_short	Biceps brachii surface EMG classification using neural networks
title_full	Biceps brachii surface EMG classification using neural networks
title_fullStr	Biceps brachii surface EMG classification using neural networks
title_full_unstemmed	Biceps brachii surface EMG classification using neural networks
title_sort	biceps brachii surface emg classification using neural networks
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/20819/1/full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/20819/2/p.%201-24.pdf
_version_	1747836771873325056

Biceps brachii surface EMG classification using neural networks

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