Early Diagnose of Autism Spectrum Disorder Using Machine Learning Based on Simple Upper Limb Movements

Wedyan, M; Al-Jumaily, A; Crippa, A

Early Diagnose of Autism Spectrum Disorder Using Machine Learning Based on Simple Upper Limb Movements

Wedyan, M

Al-Jumaily, A

Crippa, A

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Publisher:: Spronger
Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2020, 923, pp. 491-500
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Wedyan, M https://orcid.org/0000-0001-6731-7246
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463
dc.contributor.author	Crippa, A
dc.date	2018-12-13
dc.date.accessioned	2021-05-05T04:04:15Z
dc.date.available	2021-05-05T04:04:15Z
dc.date.issued	2020-01-01
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2020, 923, pp. 491-500
dc.identifier.isbn	9783030143466
dc.identifier.issn	2194-5357
dc.identifier.issn	2194-5365
dc.identifier.uri	http://hdl.handle.net/10453/148690
dc.description.abstract	The importance of early diagnosis of autism that leads to early intervention such thing shall increase the results of treating it. The Autism Spectrum Disorder (ASD) affects the children activities and caused difficulties in interaction, impairments in communication, delayed speech, and weak eye contact. These activities used as the base for ASD diagnosis decision. Children move their upper limb before some of the other activities. Moving upper limb can be based for ASD diagnosis decision for autistic children. Such paper examines diagnosing the ASD that depends on motioning the children’s upper-limb aged between two and four years based on executing specific procedures and machine learning. The approach that such study utilized is both (LDA) Linear Discriminant Analysis in order to elicit the features and (SVM) Support Vector Machines for classifying thirty children such study selected fifteen autistic children out of fifteen non-autistic children by testing the collected data that are collected from doing an easy task. The results of such study have accomplished an optimal sortation accuracy of 100% and the average accuracy of 93.8%. Such outcomes provide more proof of simple brachium motioning that might be utilized in sorting poor performance of autistic children precisely.
dc.language	en
dc.publisher	Spronger
dc.relation.ispartof	Advances in Intelligent Systems and Computing
dc.relation.ispartof	International Conference on Hybrid Intelligent Systems
dc.relation.isbasedon	10.1007/978-3-030-14347-3_48
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Early Diagnose of Autism Spectrum Disorder Using Machine Learning Based on Simple Upper Limb Movements
dc.type	Conference Proceeding
utslib.citation.volume	923
utslib.location.activity	Portugal
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/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-05T04:04:14Z
pubs.finish-date	2018-12-15
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2018-12-13
pubs.volume	923
dc.location	Switzerland

Abstract:

The importance of early diagnosis of autism that leads to early intervention such thing shall increase the results of treating it. The Autism Spectrum Disorder (ASD) affects the children activities and caused difficulties in interaction, impairments in communication, delayed speech, and weak eye contact. These activities used as the base for ASD diagnosis decision. Children move their upper limb before some of the other activities. Moving upper limb can be based for ASD diagnosis decision for autistic children. Such paper examines diagnosing the ASD that depends on motioning the children’s upper-limb aged between two and four years based on executing specific procedures and machine learning. The approach that such study utilized is both (LDA) Linear Discriminant Analysis in order to elicit the features and (SVM) Support Vector Machines for classifying thirty children such study selected fifteen autistic children out of fifteen non-autistic children by testing the collected data that are collected from doing an easy task. The results of such study have accomplished an optimal sortation accuracy of 100% and the average accuracy of 93.8%. Such outcomes provide more proof of simple brachium motioning that might be utilized in sorting poor performance of autistic children precisely.

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http://hdl.handle.net/10453/148690