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: | Proceeding Paper |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/25207/ http://irep.iium.edu.my/25207/1/06215165.pdf |
| _version_ | 1848779427047014400 |
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| author | Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran |
| author_facet | Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran |
| author_sort | Ahsan, Md. Rezwanul |
| building | IIUM Repository |
| collection | Online Access |
| description | 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%. |
| first_indexed | 2025-11-14T15:17:36Z |
| format | Proceeding Paper |
| id | iium-25207 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T15:17:36Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-252072012-09-18T06:08:58Z http://irep.iium.edu.my/25207/ Optimization of neural network for efficient EMG signal classification Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran T Technology (General) 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%. 2012 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/25207/1/06215165.pdf Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2012) Optimization of neural network for efficient EMG signal classification. In: 2012 8th International Symposium on Mechatronics and its Applications (ISMA), 10 - 12 April 2012, American University of Sharjah. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6215165&contentType=Conference+Publications |
| spellingShingle | T Technology (General) Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran Optimization of neural network for efficient EMG signal classification |
| title | Optimization of neural network for efficient EMG signal classification
|
| title_full | Optimization of neural network for efficient EMG signal classification
|
| title_fullStr | Optimization of neural network for efficient EMG signal classification
|
| title_full_unstemmed | Optimization of neural network for efficient EMG signal classification
|
| title_short | Optimization of neural network for efficient EMG signal classification
|
| title_sort | optimization of neural network for efficient emg signal classification |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/25207/ http://irep.iium.edu.my/25207/ http://irep.iium.edu.my/25207/1/06215165.pdf |