Upper limb motor coordination based early diagnosis in high risk subjects for Autism

Wedyan, M; Al-Jumaily, A

Upper limb motor coordination based early diagnosis in high risk subjects for Autism

Wedyan, M

Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 2017
Issue Date:: 2017-02-09

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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	2017-02-09	en_US
dc.identifier.citation	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 2017	en_US
dc.identifier.isbn	9781509042401	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125919
dc.description.abstract	© 2016 IEEE. Autism is a lifelong condition present from early childhood. Medical specialists' diagnosis autism based on observation is of great difficulty in communicating, difficulties for forming relationships with other people, and delayed speech. The scientists tried to discover other early signs to reach the early detection of Autism Spectrum Disorders (ASD). Early diagnosing is very important to initiate and improve treatment results. One of these signs is based on examination of upper limb motor movements. This study aims to determine whether a simple upper limb motor movement could be useful to classify High Risk (HR) infants for autism and comparison infants with Low Risk (LR) for autism. Also, this paper presents a computational intelligence method that uses HR and LR subjects between the ages of 12 and 36 months to make an early autism diagnosing. The paper examined one task which asks to insert an object into a box. It analyzed the data by using Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The results show engorging results in comparison to other state or art methods.	en_US
dc.relation.ispartof	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016	en_US
dc.relation.isbasedon	10.1109/SSCI.2016.7849893	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Upper limb motor coordination based early diagnosis in high risk subjects for Autism	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. Autism is a lifelong condition present from early childhood. Medical specialists' diagnosis autism based on observation is of great difficulty in communicating, difficulties for forming relationships with other people, and delayed speech. The scientists tried to discover other early signs to reach the early detection of Autism Spectrum Disorders (ASD). Early diagnosing is very important to initiate and improve treatment results. One of these signs is based on examination of upper limb motor movements. This study aims to determine whether a simple upper limb motor movement could be useful to classify High Risk (HR) infants for autism and comparison infants with Low Risk (LR) for autism. Also, this paper presents a computational intelligence method that uses HR and LR subjects between the ages of 12 and 36 months to make an early autism diagnosing. The paper examined one task which asks to insert an object into a box. It analyzed the data by using Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The results show engorging results in comparison to other state or art methods.

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http://hdl.handle.net/10453/125919