Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions
This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals recorded for four predefined hand moti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Versita Open, Versita Ltd. London, Great Britain
2013
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/30535/ http://irep.iium.edu.my/30535/ http://irep.iium.edu.my/30535/1/Ibrahimy.pdf |
| Summary: | This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context
of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals
recorded for four predefined hand motions have been used for neural network classifier. Different structures of neural network,
based on the number of hidden neurons and two prominent training algorithms, have been considered in the research to find out
their applicability for EMG signal classification. The classification performances are analyzed for different architectures of neural
network by considering the number of input features, number of hidden neurons, learning algorithms, correlation between
network outputs and targets, and mean square error. Between the Levenberg-Marquardt and scaled conjugate gradient learning
algorithms, the aforesaid algorithm shows better classification performance. The outcomes of the research show that the optimal
design of Levenberg-Marquardt based neural network classifier can perform well with an average classification success rate of
88.4%. A comparison of results has also been presented to validate the effectiveness of the designed neural network classifier to
discriminate EMG signals. |
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