Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix

Naik, GR; Acharyya, A; Nguyen, HT

Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix

Naik, GR

Acharyya, A Nguyen, HT

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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. 3829 - 3832
Issue Date:: 2014-11-02

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Acharyya, A	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	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. 3829 - 3832	en_US
dc.identifier.isbn	9781424479290	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34067
dc.description.abstract	© 2014 IEEE. This paper reports the classification of finger flexion and extension of surface Electromyography (EMG) and Cyberglove data using the modified Independent Component Analysis (ICA) weight matrix. The finger flexion and extension data are processed through Principal Component Analysis (PCA), and next separated using modified ICA for each individual with customized weight matrix. The extension and flexion features of sEMG and Cyberglove (extracted from modified ICA) were classified using Linear Discriminant Analysis (LDA) with near 90% classification accuracy. The applications of this study include Human Computer Interface (HCI), virtual reality and neural prosthetics.	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.2014.6944458	en_US
dc.subject.mesh	Fingers	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Discriminant Analysis	en_US
dc.subject.mesh	Principal Component Analysis	en_US
dc.subject.mesh	User-Computer Interface	en_US
dc.title	Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix	en_US
dc.type	Conference Proceeding
utslib.for	090303 Biomedical Instrumentation	en_US
utslib.for	090602 Control Systems, Robotics and Automation	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/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. This paper reports the classification of finger flexion and extension of surface Electromyography (EMG) and Cyberglove data using the modified Independent Component Analysis (ICA) weight matrix. The finger flexion and extension data are processed through Principal Component Analysis (PCA), and next separated using modified ICA for each individual with customized weight matrix. The extension and flexion features of sEMG and Cyberglove (extracted from modified ICA) were classified using Linear Discriminant Analysis (LDA) with near 90% classification accuracy. The applications of this study include Human Computer Interface (HCI), virtual reality and neural prosthetics.

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http://hdl.handle.net/10453/34067