Optimization of head movement recognition using augmented radial basis function neural network

Yuwono, M; Handojoseno, AMA; Nguyen, HT

Optimization of head movement recognition using augmented radial basis function neural network

Yuwono, M Handojoseno, AMA Nguyen, HT

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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, 2011, pp. 2776 - 2779
Issue Date:: 2011-12-26

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Field	Value	Language
dc.contributor.author	Yuwono, M	en_US
dc.contributor.author	Handojoseno, AMA	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2011-12-26	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2011, pp. 2776 - 2779	en_US
dc.identifier.isbn	9781424441211	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19263
dc.description.abstract	For people with severe spine injury, head movement recognition control has been proven to be one of the most convenient and intuitive ways to control a power wheelchair. While substantial research has been done in this area, the challenge to improve system reliability and accuracy remains due to the diversity in movement tendencies and the presence of movement artifacts. We propose a Neural-Network Configuration which we call Augmented Radial Basis Function Neural-Network (ARBF-NN). This network is constructed as a Radial Basis Function Neural-Network (RBF-NN) with a Multilayer Perceptron (MLP) augmentation layer to negate optimization limitation posed by linear classifiers in conventional RBF-NN. The RBF centroid is optimized through Regrouping Particle Swarm Optimization (RegPSO) seeded with K-Means. The trial results of ARBF-NN on Head-movement show a significant improvement on recognition accuracy up to 98.1% in sensitivity. © 2011 IEEE.	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/IEMBS.2011.6090760	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Spinal Cord Injuries	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Head Movements	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Optimization of head movement recognition using augmented radial basis function neural network	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Boston, Massachusetts, USA	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

Abstract:

For people with severe spine injury, head movement recognition control has been proven to be one of the most convenient and intuitive ways to control a power wheelchair. While substantial research has been done in this area, the challenge to improve system reliability and accuracy remains due to the diversity in movement tendencies and the presence of movement artifacts. We propose a Neural-Network Configuration which we call Augmented Radial Basis Function Neural-Network (ARBF-NN). This network is constructed as a Radial Basis Function Neural-Network (RBF-NN) with a Multilayer Perceptron (MLP) augmentation layer to negate optimization limitation posed by linear classifiers in conventional RBF-NN. The RBF centroid is optimized through Regrouping Particle Swarm Optimization (RegPSO) seeded with K-Means. The trial results of ARBF-NN on Head-movement show a significant improvement on recognition accuracy up to 98.1% in sensitivity. © 2011 IEEE.

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http://hdl.handle.net/10453/19263