Extreme learning machine based sEMG for drop-foot after stroke detection

Adil, S; Anwar, T; Adel; Jumaily, A

Extreme learning machine based sEMG for drop-foot after stroke detection

Adil, S Anwar, T Adel Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: 6th International Conference on Information Science and Technology, ICIST 2016, 2016, pp. 18 - 22
Issue Date:: 2016-06-01

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Field	Value	Language
dc.contributor.author	Adil, S	en_US
dc.contributor.author	Anwar, T	en_US
dc.contributor.author	Adel	en_US
dc.contributor.author	Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2016-06-01	en_US
dc.identifier.citation	6th International Conference on Information Science and Technology, ICIST 2016, 2016, pp. 18 - 22	en_US
dc.identifier.isbn	9781509012206	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123084
dc.description.abstract	© 2016 IEEE. Drop Foot (FD) is the inability to raising the foot due to the weakness of paralysis dorsiflexion muscle, this disability is caused by stroke. Recognize FD of the patient and provide a treatment as they needed is an important requirement for rehabilitation. One of the recognize techniques for FD event is based on using the surface electromyography (sEMG) signal. Utilizing sEMG signal can help to provide patient the specific rehabilitation treatment in specific time since it can detect the FD event before it happen. This paper has investigated the ability of Extreme Learning Machine (ELM) method to classify the sickness and healthy muscles on the leg, based on sEMG, that yield the FD. The performance is compared with Support Vector Machine (SVM) and Neural Network (NN). Classification accuracy with ELM is much better than SVM and NN giving the results with up to 97% classification accuracy using two channels on each side of the leg.	en_US
dc.relation.ispartof	6th International Conference on Information Science and Technology, ICIST 2016	en_US
dc.relation.isbasedon	10.1109/ICIST.2016.7483378	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Extreme learning machine based sEMG for drop-foot after stroke detection	en_US
dc.type	Conference Proceeding
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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Drop Foot (FD) is the inability to raising the foot due to the weakness of paralysis dorsiflexion muscle, this disability is caused by stroke. Recognize FD of the patient and provide a treatment as they needed is an important requirement for rehabilitation. One of the recognize techniques for FD event is based on using the surface electromyography (sEMG) signal. Utilizing sEMG signal can help to provide patient the specific rehabilitation treatment in specific time since it can detect the FD event before it happen. This paper has investigated the ability of Extreme Learning Machine (ELM) method to classify the sickness and healthy muscles on the leg, based on sEMG, that yield the FD. The performance is compared with Support Vector Machine (SVM) and Neural Network (NN). Classification accuracy with ELM is much better than SVM and NN giving the results with up to 97% classification accuracy using two channels on each side of the leg.

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

http://hdl.handle.net/10453/123084