Swarm-wavelet based extreme learning machine for finger movement classification on transradial amputees

Anam, K; Al-Jumaily, A

Swarm-wavelet based extreme learning machine for finger movement classification on transradial amputees

Anam, K

Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, 2014, pp. 4192 - 4195
Issue Date:: 2014-11-02

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Field	Value	Language
dc.contributor.author	Anam, K https://orcid.org/0000-0003-4169-2237	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2014-11-02	en_US
dc.identifier.citation	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, 2014, pp. 4192 - 4195	en_US
dc.identifier.isbn	9781424479290	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30173
dc.description.abstract	© 2014 IEEE. The use of a small number of surface electromyography (EMG) channels on the transradial amputee in a myoelectric controller is a big challenge. This paper proposes a pattern recognition system using an extreme learning machine (ELM) optimized by particle swarm optimization (PSO). PSO is mutated by wavelet function to avoid trapped in a local minima. The proposed system is used to classify eleven imagined finger motions on five amputees by using only two EMG channels. The optimal performance of wavelet-PSO was compared to a grid-search method and standard PSO. The experimental results show that the proposed system is the most accurate classifier among other tested classifiers. It could classify 11 finger motions with the average accuracy of about 94 % across five amputees.	en_US
dc.relation.ispartof	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014	en_US
dc.relation.isbasedon	10.1109/EMBC.2014.6944548	en_US
dc.subject.mesh	Hand	en_US
dc.subject.mesh	Fingers	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Analysis of Variance	en_US
dc.subject.mesh	Movement	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Motion	en_US
dc.subject.mesh	Nontherapeutic Human Experimentation	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Amputees	en_US
dc.title	Swarm-wavelet based extreme learning machine for finger movement classification on transradial amputees	en_US
dc.type	Conference Proceeding
utslib.for	090609 Signal Processing	en_US
dc.location.activity	Chicago, USA
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:

© 2014 IEEE. The use of a small number of surface electromyography (EMG) channels on the transradial amputee in a myoelectric controller is a big challenge. This paper proposes a pattern recognition system using an extreme learning machine (ELM) optimized by particle swarm optimization (PSO). PSO is mutated by wavelet function to avoid trapped in a local minima. The proposed system is used to classify eleven imagined finger motions on five amputees by using only two EMG channels. The optimal performance of wavelet-PSO was compared to a grid-search method and standard PSO. The experimental results show that the proposed system is the most accurate classifier among other tested classifiers. It could classify 11 finger motions with the average accuracy of about 94 % across five amputees.

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http://hdl.handle.net/10453/30173