Optimized kernel extreme learning machine for myoelectric pattern recognition

Anam, K; Al-Jumaily, A

Optimized kernel extreme learning machine for myoelectric pattern recognition

Anam, K

Al-Jumaily, A

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Publication Type:: Journal Article
Citation:: International Journal of Electrical and Computer Engineering, 2018, 8 (1), pp. 483 - 496
Issue Date:: 2018-02-01

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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	2018-02-01	en_US
dc.identifier.citation	International Journal of Electrical and Computer Engineering, 2018, 8 (1), pp. 483 - 496	en_US
dc.identifier.issn	2088-8708	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132416
dc.description.abstract	© 2018 Institute of Advanced Engineering and Science. All rights reserved. Myoelectric pattern recognition (MPR) is used to detect user's intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).	en_US
dc.relation.ispartof	International Journal of Electrical and Computer Engineering	en_US
dc.relation.isbasedon	10.11591/ijece.v8i1.pp483-496	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Optimized kernel extreme learning machine for myoelectric pattern recognition	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	8	en_US
utslib.for	0906 Electrical and Electronic Engineering	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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

© 2018 Institute of Advanced Engineering and Science. All rights reserved. Myoelectric pattern recognition (MPR) is used to detect user's intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN).

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http://hdl.handle.net/10453/132416