AW-ELM-based Crouch Gait recognition after ischemic stroke

Adil, S; Jumaily, AA; Anam, K

AW-ELM-based Crouch Gait recognition after ischemic stroke

Adil, S Jumaily, AA

Anam, K

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Publication Type:: Conference Proceeding
Citation:: International Conference on Electronic Devices, Systems, and Applications, 2017
Issue Date:: 2017-01-13

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Field	Value	Language
dc.contributor.author	Adil, S	en_US
dc.contributor.author	Jumaily, AA https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Anam, K https://orcid.org/0000-0003-4169-2237	en_US
dc.date.issued	2017-01-13	en_US
dc.identifier.citation	International Conference on Electronic Devices, Systems, and Applications, 2017	en_US
dc.identifier.isbn	9781509053063	en_US
dc.identifier.issn	2159-2047	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126489
dc.description.abstract	© 2016 IEEE. Crouch Gait (CG) can be observed in the hemiplegia persons after ischemic stroke. Walking with Crouch Gait (CG) shown a large gaits disorder. This paper explores the use of adaptive wavelet extreme learning machine (AW-ELM) to classifying different gait conditions for hemiplegia and healthy subjects. Three participants having a Crouch Gait problem with categories of Mild, Moderate, and Severe gait conditions, also, one Healthy person are used their data in this work. The recognition system extracting number of time and frequency domain features for dimensionality reduction. While for the classification stage, the common Extreme Learning Machine (ELM) classifiers are used. AW-ELM achieved maximum testing accuracy up to 91.149 % and with using majority vote post-processing the accuracy achieves 91.547 %.	en_US
dc.relation.ispartof	International Conference on Electronic Devices, Systems, and Applications	en_US
dc.relation.isbasedon	10.1109/ICEDSA.2016.7818552	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	AW-ELM-based Crouch Gait recognition after ischemic stroke	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical 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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Crouch Gait (CG) can be observed in the hemiplegia persons after ischemic stroke. Walking with Crouch Gait (CG) shown a large gaits disorder. This paper explores the use of adaptive wavelet extreme learning machine (AW-ELM) to classifying different gait conditions for hemiplegia and healthy subjects. Three participants having a Crouch Gait problem with categories of Mild, Moderate, and Severe gait conditions, also, one Healthy person are used their data in this work. The recognition system extracting number of time and frequency domain features for dimensionality reduction. While for the classification stage, the common Extreme Learning Machine (ELM) classifiers are used. AW-ELM achieved maximum testing accuracy up to 91.149 % and with using majority vote post-processing the accuracy achieves 91.547 %.

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http://hdl.handle.net/10453/126489