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...
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| Language: | English |
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2012
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| 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 |
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iium-259652013-01-21T05:29:27Z http://irep.iium.edu.my/25965/ The use of artificial neural network in the classification of EMG signals Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran T Technology (General) 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%. 2012 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/25965/1/06305853_FTRA.pdf Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2012) The use of artificial neural network in the classification of EMG signals. In: The 3rd FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing (MUSIC '12), 26-28 June 2012, Vancouver, Canada. http://www.ftrai.org/music2012/ doi:10.1109/MUSIC.2012.46 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
T Technology (General) |
| spellingShingle |
T Technology (General) Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran The use of artificial neural network in the classification of EMG signals |
| description |
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%. |
| format |
Conference or Workshop Item |
| 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 |
| title |
The use of artificial neural network in the classification of EMG signals |
| title_short |
The use of artificial neural network in the classification of EMG signals |
| title_full |
The use of artificial neural network in the classification of EMG signals |
| title_fullStr |
The use of artificial neural network in the classification of EMG signals |
| title_full_unstemmed |
The use of artificial neural network in the classification of EMG signals |
| title_sort |
use of artificial neural network in the classification of emg signals |
| publishDate |
2012 |
| url |
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 |
| first_indexed |
2023-09-18T20:38:43Z |
| last_indexed |
2023-09-18T20:38:43Z |
| _version_ |
1777409220390420480 |