Swarm-based extreme learning machine for finger movement recognition

Anam, K; Al-Jumaily, A

Swarm-based extreme learning machine for finger movement recognition

Anam, K

Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: Middle East Conference on Biomedical Engineering, MECBME, 2014, pp. 273 - 276
Issue Date:: 2014-01-01

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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	2014-01-01	en_US
dc.identifier.citation	Middle East Conference on Biomedical Engineering, MECBME, 2014, pp. 273 - 276	en_US
dc.identifier.isbn	9781479947997	en_US
dc.identifier.issn	2165-4247	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30175
dc.description.abstract	An accurate finger movement recognition is required in many robotics prosthetics and assistive hand devices. The use of a small number of Electromyography (EMG) channels for classifying the finger movement is a challenging task. This paper proposes a novel recognition system which employs Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, kernel-based Extreme Learning Machine (ELM) for classification and the majority vote for classification smoothness. Particle Swarm Optimization (PSO) is used to optimize the kernel-based ELM. Three hybridizations with three kernels, radial basis function (SRBF-ELM), linear (SLIN-ELM), and polynomial (SPOLY-ELM) are introduced. The experimental results show that SRBF-ELM significantly outperforms SLIN-ELM but not too much different compared to SPOLY-LIN. Moreover, PSO is able to optimize the three systems by giving the accuracy more than 90% with the highest accuracy is ∼94%. © 2014 IEEE.	en_US
dc.relation.ispartof	Middle East Conference on Biomedical Engineering, MECBME	en_US
dc.relation.isbasedon	10.1109/MECBME.2014.6783257	en_US
dc.title	Swarm-based extreme learning machine for finger movement recognition	en_US
dc.type	Conference Proceeding
utslib.for	090609 Signal Processing	en_US
dc.location.activity	Doha, Qatar
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:

An accurate finger movement recognition is required in many robotics prosthetics and assistive hand devices. The use of a small number of Electromyography (EMG) channels for classifying the finger movement is a challenging task. This paper proposes a novel recognition system which employs Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, kernel-based Extreme Learning Machine (ELM) for classification and the majority vote for classification smoothness. Particle Swarm Optimization (PSO) is used to optimize the kernel-based ELM. Three hybridizations with three kernels, radial basis function (SRBF-ELM), linear (SLIN-ELM), and polynomial (SPOLY-ELM) are introduced. The experimental results show that SRBF-ELM significantly outperforms SLIN-ELM but not too much different compared to SPOLY-LIN. Moreover, PSO is able to optimize the three systems by giving the accuracy more than 90% with the highest accuracy is ∼94%. © 2014 IEEE.

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http://hdl.handle.net/10453/30175