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EMG motion pattern classification through design and optimization of Neural Network

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Main Authors: Md. Rezwanul, Ahsan, Muhammad Ibn, Ibrahimy, Othman Omran, Khalifa
Other Authors: ibrahimy@iium.edu.my
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Electromyography (EMG) Signal
Neural Network
Electromyography (EMG) Motion Pattern
Electromyography (EMG) Signal Classification
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/21297
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spelling my.unimap-212972012-10-11T01:16:12Z EMG motion pattern classification through design and optimization of Neural Network Md. Rezwanul, Ahsan Muhammad Ibn, Ibrahimy Othman Omran, Khalifa ibrahimy@iium.edu.my Electromyography (EMG) Signal Neural Network Electromyography (EMG) Motion Pattern Electromyography (EMG) Signal Classification Link to publisher's homepage at http://ieeexplore.ieee.org/ This paper illustrates the classification of EMG signals through design and optimization of Artificial Neural Network (ANN). Different types of ANN models are basically structured with many interconnected network elements which can develop pattern classification strategies based on a set of input/training data. The ANN models work in parallel thus providing higher computational performance than traditional classifiers which function sequentially. The EMG signals obtained for different kinds of hand motions, which further denoised and processed to extract the features. Extracted time and time-frequency based feature sets are used to train the neural network. A back-propagation neural network with Levenberg-Marquardt training algorithm has been utilized for the classification of EMG signals. The results show that the designed network is optimized for 10 hidden neurons with 7 input features and able to efficiently classify single channel EMG signals with an average success rate of 88.4%. 2012-10-11T01:16:12Z 2012-10-11T01:16:12Z 2012-02-27 Working Paper p. 175-179 978-145771989-9 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6179000 http://hdl.handle.net/123456789/21297 en Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012) Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Electromyography (EMG) Signal
Neural Network
Electromyography (EMG) Motion Pattern
Electromyography (EMG) Signal Classification
spellingShingle Electromyography (EMG) Signal
Neural Network
Electromyography (EMG) Motion Pattern
Electromyography (EMG) Signal Classification
Md. Rezwanul, Ahsan
Muhammad Ibn, Ibrahimy
Othman Omran, Khalifa
EMG motion pattern classification through design and optimization of Neural Network
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 ibrahimy@iium.edu.my
author_facet ibrahimy@iium.edu.my
Md. Rezwanul, Ahsan
Muhammad Ibn, Ibrahimy
Othman Omran, Khalifa
format Working Paper
author Md. Rezwanul, Ahsan
Muhammad Ibn, Ibrahimy
Othman Omran, Khalifa
author_sort Md. Rezwanul, Ahsan
title EMG motion pattern classification through design and optimization of Neural Network
title_short EMG motion pattern classification through design and optimization of Neural Network
title_full EMG motion pattern classification through design and optimization of Neural Network
title_fullStr EMG motion pattern classification through design and optimization of Neural Network
title_full_unstemmed EMG motion pattern classification through design and optimization of Neural Network
title_sort emg motion pattern classification through design and optimization of neural network
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/21297
_version_ 1643793338515587072
score 13.214268

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