AW-ELM-based Crouch Gait Recognition after ischemic stroke

dc.contributor.author	Adil, Sahar
dc.contributor.author	Al-Jumaily, Adel
dc.contributor.author	Anam, Khairul
dc.date.accessioned	2018-04-04T03:15:32Z
dc.date.available	2018-04-04T03:15:32Z
dc.date.issued	2018-04-04
dc.identifier.isbn	978-1-5090-5306-3
dc.identifier.uri	http://repository.unej.ac.id/handle/123456789/85194
dc.description	Proceedings 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA)	en_US
dc.description.abstract	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.language.iso	en	en_US
dc.subject	AW-ELM	en_US
dc.subject	ischemic stroke	en_US
dc.subject	Crouch Gait	en_US
dc.title	AW-ELM-based Crouch Gait Recognition after ischemic stroke	en_US
dc.type	Prosiding	en_US