Early diagnosis autism based on upper limb motor coordination in high risk subjects for autism

Wedyan, M; Al-Jumaily, A

Early diagnosis autism based on upper limb motor coordination in high risk subjects for autism

Wedyan, M

Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: IRIS 2016 - 2016 IEEE 4th International Symposium on Robotics and Intelligent Sensors: Empowering Robots with Smart Sensors, 2017, pp. 13 - 18
Issue Date:: 2017-10-11

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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	2017-10-11	en_US
dc.identifier.citation	IRIS 2016 - 2016 IEEE 4th International Symposium on Robotics and Intelligent Sensors: Empowering Robots with Smart Sensors, 2017, pp. 13 - 18	en_US
dc.identifier.isbn	9781509060849	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127410
dc.description.abstract	© 2016 IEEE. Detection of autism in the beginning of infant life is very important to increase child treatment outcomes. There are many signs that help to diagnosis autism based on observation of great difficulty in communicating, delayed speech, and difficulty in forming relationships with other people. Attempts to discover other early symptoms and signs do not stop and tried to reach the early detection of autism. One of these signs is based on examination of child motor movements, such as kinematic for both gait and upper limb. This study aims to know how a basic upper-limb motion would be helpful to accurately classify High-Risk (HR) Infants for autism and infants with Low-Risk (LR) for autism. Also, the paper examined the task that throws a small ball into a transparent plastic tub followed by inserting the ball into a clear tube open at both ends performed by HR and LR subjects between the ages of 12 and 36 months. Then the data was analyzed by using Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The results showed encouraging results in comparison to other state or art.	en_US
dc.relation.ispartof	IRIS 2016 - 2016 IEEE 4th International Symposium on Robotics and Intelligent Sensors: Empowering Robots with Smart Sensors	en_US
dc.relation.isbasedon	10.1109/IRIS.2016.8066059	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Early diagnosis autism based on upper limb motor coordination 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. Detection of autism in the beginning of infant life is very important to increase child treatment outcomes. There are many signs that help to diagnosis autism based on observation of great difficulty in communicating, delayed speech, and difficulty in forming relationships with other people. Attempts to discover other early symptoms and signs do not stop and tried to reach the early detection of autism. One of these signs is based on examination of child motor movements, such as kinematic for both gait and upper limb. This study aims to know how a basic upper-limb motion would be helpful to accurately classify High-Risk (HR) Infants for autism and infants with Low-Risk (LR) for autism. Also, the paper examined the task that throws a small ball into a transparent plastic tub followed by inserting the ball into a clear tube open at both ends performed by HR and LR subjects between the ages of 12 and 36 months. Then the data was analyzed by using Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The results showed encouraging results in comparison to other state or art.

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http://hdl.handle.net/10453/127410