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A comparative study of wavelet families for classification of wrist motions

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Main Authors: Muthusamy, Hariharan, Chong, Yen Fook, Sindhu, Ravindran, Bukhari, Ilias, Sazali, Yaacob, Prof. Dr.
Other Authors: hari@unimap.edu.my
Format: Article
Language:English
Published: Elsevier Ltd. 2013
Subjects:
Discrete wavelet transforms
Wrist motions
Neural networks
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/26407
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spelling my.unimap-264072013-07-02T08:32:57Z A comparative study of wavelet families for classification of wrist motions Muthusamy, Hariharan Chong, Yen Fook Sindhu, Ravindran Bukhari, Ilias Sazali, Yaacob, Prof. Dr. hari@unimap.edu.my Discrete wavelet transforms Wrist motions Neural networks Link to publisher's homepage at http://www.elsevier.com/ The selection of most suitable mother wavelet function is still an open research problem in various signal and image processing applications. This paper presents a comparative study of different wavelet families (Daubechies, Symlets, Coiflets, and Biorthogonal) for analysis of wrist motions from electromyography (EMG) signals. EMG signals are decomposed into three levels using discrete wavelet packet transform. From the decomposed EMG signals, root mean square (RMS) value, autoregressive (AR) model coefficients (4th order) and waveform length (WL) are extracted. Two data projection methods such as principal component analysis (PCA) and linear disciminant analysis (LDA) are used to reduce the dimensionality of the extracted features. Probabilistic neural network (PNN) and general regression neural network (GRNN) are employed to classify the different types of wrist motions, which gives a promising accuracy of above 99%. From the analysis, we inferred that 'Biorthogonal' and 'Coiflets' wavelet families are more suitable for accurate classification of EMG signals of different wrist motions. 2013-07-02T08:32:57Z 2013-07-02T08:32:57Z 2012-11 Article Computers and Electrical Engineering, vol. 38(6), 2012, pages 1798-1807 0045-7906 http://www.sciencedirect.com/science/article/pii/S0045790612001656 http://hdl.handle.net/123456789/26407 en Elsevier Ltd.
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 Discrete wavelet transforms
Wrist motions
Neural networks
spellingShingle Discrete wavelet transforms
Wrist motions
Neural networks
Muthusamy, Hariharan
Chong, Yen Fook
Sindhu, Ravindran
Bukhari, Ilias
Sazali, Yaacob, Prof. Dr.
A comparative study of wavelet families for classification of wrist motions
description Link to publisher's homepage at http://www.elsevier.com/
author2 hari@unimap.edu.my
author_facet hari@unimap.edu.my
Muthusamy, Hariharan
Chong, Yen Fook
Sindhu, Ravindran
Bukhari, Ilias
Sazali, Yaacob, Prof. Dr.
format Article
author Muthusamy, Hariharan
Chong, Yen Fook
Sindhu, Ravindran
Bukhari, Ilias
Sazali, Yaacob, Prof. Dr.
author_sort Muthusamy, Hariharan
title A comparative study of wavelet families for classification of wrist motions
title_short A comparative study of wavelet families for classification of wrist motions
title_full A comparative study of wavelet families for classification of wrist motions
title_fullStr A comparative study of wavelet families for classification of wrist motions
title_full_unstemmed A comparative study of wavelet families for classification of wrist motions
title_sort comparative study of wavelet families for classification of wrist motions
publisher Elsevier Ltd.
publishDate 2013
url http://dspace.unimap.edu.my/xmlui/handle/123456789/26407
_version_ 1643794934371713024
score 13.214268

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