Nonlinear multiscale Maximal Lyapunov Exponent for accurate myoelectric signal classification

Guo, Y; Naik, GR; Huang, S; Abraham, A; Nguyen, HT

Nonlinear multiscale Maximal Lyapunov Exponent for accurate myoelectric signal classification

Guo, Y Naik, GR

Huang, S Abraham, A Nguyen, HT

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Publication Type:: Journal Article
Citation:: Applied Soft Computing Journal, 2015, 36 pp. 633 - 640
Issue Date:: 2015-08-24

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Field	Value	Language
dc.contributor.author	Guo, Y	en_US
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Huang, S	en_US
dc.contributor.author	Abraham, A	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2015-08-24	en_US
dc.identifier.citation	Applied Soft Computing Journal, 2015, 36 pp. 633 - 640	en_US
dc.identifier.issn	1568-4946	en_US
dc.identifier.uri	http://hdl.handle.net/10453/40743
dc.description.abstract	© 2015 Elsevier B.V. All rights reserved. Surface Electromyography (sEMG) is a non-invasive, easy to record signal of superficial muscles from the skin surface. The sEMG is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. Considering the nonlinear and non-stationary characteristics of sEMG, hand gesture recognition using sEMG signals necessitate designers to use Maximal Lyapunov Exponent (MLE) or ensemble Empirical Mode Decomposition (EMD) based MLEs. In this research, we propose a hand gesture recognition method of sEMG based on nonlinear multiscale MLE. The aim is to increase the classification accuracy of sEMG features while reducing the complexity of EMD. The nonlinear MLE features are classified using Flexible Neural Tree (FNT), which can solve highly structured dependent problems of the Artificial Neural Network (ANN). The testing has been conducted using several experiments with five participants. The classification performance of nonlinear multiscale MLE method is compared with MLE and EMD-based MLE through simulations. Experimental results demonstrate that the former algorithm outperforms the two latter algorithms and can classify six different hand gestures up to 97.6% accuracy.	en_US
dc.relation.ispartof	Applied Soft Computing Journal	en_US
dc.relation.isbasedon	10.1016/j.asoc.2015.07.032	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Nonlinear multiscale Maximal Lyapunov Exponent for accurate myoelectric signal classification	en_US
dc.type	Journal Article
utslib.citation.volume	36	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	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/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

© 2015 Elsevier B.V. All rights reserved. Surface Electromyography (sEMG) is a non-invasive, easy to record signal of superficial muscles from the skin surface. The sEMG is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. Considering the nonlinear and non-stationary characteristics of sEMG, hand gesture recognition using sEMG signals necessitate designers to use Maximal Lyapunov Exponent (MLE) or ensemble Empirical Mode Decomposition (EMD) based MLEs. In this research, we propose a hand gesture recognition method of sEMG based on nonlinear multiscale MLE. The aim is to increase the classification accuracy of sEMG features while reducing the complexity of EMD. The nonlinear MLE features are classified using Flexible Neural Tree (FNT), which can solve highly structured dependent problems of the Artificial Neural Network (ANN). The testing has been conducted using several experiments with five participants. The classification performance of nonlinear multiscale MLE method is compared with MLE and EMD-based MLE through simulations. Experimental results demonstrate that the former algorithm outperforms the two latter algorithms and can classify six different hand gestures up to 97.6% accuracy.

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http://hdl.handle.net/10453/40743