Adaptive myoelectric pattern recognition for arm movement in different positions using advanced online sequential extreme learning machine

Anam, K; Al-Jumaily, A

Adaptive myoelectric pattern recognition for arm movement in different positions using advanced online sequential extreme learning machine

Anam, K

Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, 2016-October pp. 900 - 903
Issue Date:: 2016-10-13

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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	2016-10-13	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, 2016-October pp. 900 - 903	en_US
dc.identifier.isbn	9781457702204	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122862
dc.description.abstract	© 2016 IEEE. The performance of the myoelectric pattern recognition system sharply decreases when working in various limb positions. The issue can be solved by cumbersome training procedure that can anticipate all possible future situations. However, this procedure will sacrifice the comfort of the user. In addition, many unpredictable scenarios may be met in the future. This paper proposed a new adaptive myoelectric pattern recognition using advance online sequential extreme learning (AOS-ELM) for classification of the hand movements to five different positions. AOS-ELM is an improvement of OS-ELM that can verify the adaptation validity using entropy. The proposed adaptive MPR was able to classify eight different classes from eleven subjects by accuracy of 95.42 % using data from one position. After learning the data from whole positions, the performance of the proposed system is 86.13 %. This performance was better than the MPR that employed original OS-ELM, but it was worse than the MPR that utilized the batch classifiers. Nevertheless, the adaptation mechanism of AOS-ELM is preferred in the real-time application.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2016.7590846	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.mesh	Arm	en_US
dc.subject.mesh	Hand	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Motion	en_US
dc.subject.mesh	Entropy	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Machine Learning	en_US
dc.title	Adaptive myoelectric pattern recognition for arm movement in different positions using advanced online sequential extreme learning machine	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-October	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	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
pubs.volume	2016-October	en_US

Abstract:

© 2016 IEEE. The performance of the myoelectric pattern recognition system sharply decreases when working in various limb positions. The issue can be solved by cumbersome training procedure that can anticipate all possible future situations. However, this procedure will sacrifice the comfort of the user. In addition, many unpredictable scenarios may be met in the future. This paper proposed a new adaptive myoelectric pattern recognition using advance online sequential extreme learning (AOS-ELM) for classification of the hand movements to five different positions. AOS-ELM is an improvement of OS-ELM that can verify the adaptation validity using entropy. The proposed adaptive MPR was able to classify eight different classes from eleven subjects by accuracy of 95.42 % using data from one position. After learning the data from whole positions, the performance of the proposed system is 86.13 %. This performance was better than the MPR that employed original OS-ELM, but it was worse than the MPR that utilized the batch classifiers. Nevertheless, the adaptation mechanism of AOS-ELM is preferred in the real-time application.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/122862