An investigation of upper limb motor task based discriminate for high risk autism

Wedyan, M; Al-Jumaily, A

An investigation of upper limb motor task based discriminate for high risk autism

Wedyan, M

Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, 2017, 2018-January pp. 1 - 6
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Wedyan, M https://orcid.org/0000-0001-6731-7246	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, 2017, 2018-January pp. 1 - 6	en_US
dc.identifier.isbn	9781538618295	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127401
dc.description.abstract	© 2017 IEEE. Early diagnosis of children with autism means early intervention which is very important to increase child treatment outcomes. One of these signs is based on examination of child upper limb movements. This paper presents an evaluation of tasks methods for investigation of upper limb motor in children at High Risk (HR) for autism. This study examined three tasks and finds the upper limb motor type that discriminates the high risk of autism. These three tasks are: the first task is a throw a small ball into a transparent plastic followed by insert the ball into a clear tube open at both sides. In the second task, place a block into a large open box then place four similar blocks on a target block to make a tower and in the third task is put in a shape into a small fund with removable lids. The paper introduces using machine learning to discriminate between the children with high risk for autism. The feature extracting techniques Linear Discriminant Analysis (LDA) is used. After generating feature vectors, Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are used for classification step. The results are very encouraging the maximum classification accuracy for a task that inserts the ball into a clear tube open at both sides with mean accuracy 75.0% and 81.67 with SVMs and ELM respectively.	en_US
dc.relation.ispartof	Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017	en_US
dc.relation.isbasedon	10.1109/ISKE.2017.8258753	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	An investigation of upper limb motor task based discriminate for high risk autism	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
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
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. Early diagnosis of children with autism means early intervention which is very important to increase child treatment outcomes. One of these signs is based on examination of child upper limb movements. This paper presents an evaluation of tasks methods for investigation of upper limb motor in children at High Risk (HR) for autism. This study examined three tasks and finds the upper limb motor type that discriminates the high risk of autism. These three tasks are: the first task is a throw a small ball into a transparent plastic followed by insert the ball into a clear tube open at both sides. In the second task, place a block into a large open box then place four similar blocks on a target block to make a tower and in the third task is put in a shape into a small fund with removable lids. The paper introduces using machine learning to discriminate between the children with high risk for autism. The feature extracting techniques Linear Discriminant Analysis (LDA) is used. After generating feature vectors, Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are used for classification step. The results are very encouraging the maximum classification accuracy for a task that inserts the ball into a clear tube open at both sides with mean accuracy 75.0% and 81.67 with SVMs and ELM respectively.

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http://hdl.handle.net/10453/127401