Novel feature extraction method based on fuzzy entropy and wavelet packet transform for myoelectric control

Khushaba, RN; Al-Jumaily, A; Al-Ani, A

Novel feature extraction method based on fuzzy entropy and wavelet packet transform for myoelectric control

Khushaba, RN

Al-Jumaily, A

Al-Ani, A

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Publication Type:: Conference Proceeding
Citation:: ISCIT 2007 - 2007 International Symposium on Communications and Information Technologies Proceedings, 2007, pp. 352 - 357
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	ISCIT 2007 - 2007 International Symposium on Communications and Information Technologies Proceedings, 2007, pp. 352 - 357	en_US
dc.identifier.isbn	1424409772	en_US
dc.identifier.isbn	9781424409778	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2945
dc.description.abstract	In this paper, a novel feature extraction method based on the utilization of wavelet packet transform (WPT) and the concept of fuzzy entropy is presented. The method acts in steps, were in the first step the WPT is employed to generate a wavelet decomposition tree from which many features are extracted. In the second step, a new algorithm to compute the fuzzy entropy is developed and adopted as a measure of information content to judge on features suitability in classification, by setting a threshold and removing the features that fall under a certain threshold. In the third step, principle component analysis (PCA) is employed to reduce the dimensionality of the generated feature set. As an application, the new algorithm is employed in multifunction myoelectric control problem to prove its efficiency. Accurate results (99% accuracy) are acquired from using only a small subset of the original feature set generated by the wavelet tree. The obtained results indicate that the generated features are of maximum relevance and with minimum degree of redundancy. © 2007 IEEE.	en_US
dc.relation.ispartof	ISCIT 2007 - 2007 International Symposium on Communications and Information Technologies Proceedings	en_US
dc.relation.isbasedon	10.1109/ISCIT.2007.4392044	en_US
dc.title	Novel feature extraction method based on fuzzy entropy and wavelet packet transform for myoelectric control	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
dc.location.activity	Sydney, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, a novel feature extraction method based on the utilization of wavelet packet transform (WPT) and the concept of fuzzy entropy is presented. The method acts in steps, were in the first step the WPT is employed to generate a wavelet decomposition tree from which many features are extracted. In the second step, a new algorithm to compute the fuzzy entropy is developed and adopted as a measure of information content to judge on features suitability in classification, by setting a threshold and removing the features that fall under a certain threshold. In the third step, principle component analysis (PCA) is employed to reduce the dimensionality of the generated feature set. As an application, the new algorithm is employed in multifunction myoelectric control problem to prove its efficiency. Accurate results (99% accuracy) are acquired from using only a small subset of the original feature set generated by the wavelet tree. The obtained results indicate that the generated features are of maximum relevance and with minimum degree of redundancy. © 2007 IEEE.

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http://hdl.handle.net/10453/2945