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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| Format: | Thesis |
| Language: | English |
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| 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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| Summary: | 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. |
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