The use of artificial neural network in the classification of EMG signals
This paper presents the design, optimization and performance evaluation of artificial neural network for the efficient classification of Electromyography (EMG) signals. The EMG signals are collected for different types of volunteer hand motion which are processed to extract some predefined...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/25965/ http://irep.iium.edu.my/25965/ http://irep.iium.edu.my/25965/ http://irep.iium.edu.my/25965/1/06305853_FTRA.pdf |
| Summary: | This paper presents the design, optimization and
performance evaluation of artificial neural network for the
efficient classification of Electromyography (EMG) signals.
The EMG signals are collected for different types of volunteer
hand motion which are processed to extract some predefined
features as inputs to the neural network. The time and timefrequency based extracted feature sets are used to train the
neural network. A back-propagation neural network with
Levenberg-Marquardt training algorithm has been employed
for the classification of EMG signals. The results show that the
designed and optimized network able to classify single channel
EMG signals with an average success rate of 88.4%. |
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