Optimization of neural network for efficient EMG signal classification
This paper illustrates the classification of Electromyography (EMG) signals through designing and optimization of artificial neural network. The EMG signals obtained for different kinds of hand movements, which are processed to extract the features. Extracted time and timefrequency based fea...
| 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/25207/ http://irep.iium.edu.my/25207/ http://irep.iium.edu.my/25207/1/06215165.pdf |
| Summary: | This paper illustrates the classification of
Electromyography (EMG) signals through designing and
optimization of artificial neural network. The EMG signals
obtained for different kinds of hand movements, which are
processed to extract the features. Extracted time and timefrequency based feature sets are used to train the neural
network. A back-propagation neural network with LevenbergMarquardt training algorithm has been utilized for the
classification. The results show that the designed network is
optimized for 10 hidden neurons and able to efficiently classify
single channel EMG signals with an average rate of 88.4%. |
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