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dc.contributor.authorYaneva, Victoria
dc.contributor.authorHa, Le An
dc.contributor.authorEraslan, Sukru
dc.contributor.authorYesilada, Yeliz
dc.contributor.authorMitkov, Ruslan
dc.date.accessioned2020-06-09T10:31:15Z
dc.date.available2020-06-09T10:31:15Z
dc.date.issued2020-04-30
dc.identifier.citationYaneva, V., Ha, L.A., Eraslan, S., Yesilada, Y. and Mitkov, R. (2020) Detecting high-functioning autism in adults using eye tracking and machine learning, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(6), pp. 1254 - 1261.en
dc.identifier.issn1534-4320en
dc.identifier.pmid32356755 (pubmed)
dc.identifier.doi10.1109/tnsre.2020.2991675en
dc.identifier.urihttp://hdl.handle.net/2436/623246
dc.descriptionThis is an accepted manuscript of an article published by IEEE in IEEE Transactions on Neural Systems and Rehabilitation Engineering on 30/04/2020, available online: https://ieeexplore.ieee.org/document/9082703 The accepted version of the publication may differ from the final published version.en
dc.description.abstractThe purpose of this study is to test whether visual processing differences between adults with and without highfunctioning autism captured through eye tracking can be used to detect autism. We record the eye movements of adult participants with and without autism while they look for information within web pages. We then use the recorded eye-tracking data to train machine learning classifiers to detect the condition. The data was collected as part of two separate studies involving a total of 71 unique participants (31 with autism and 40 control), which enabled the evaluation of the approach on two separate groups of participants, using different stimuli and tasks. We explore the effects of a number of gaze-based and other variables, showing that autism can be detected automatically with around 74% accuracy. These results confirm that eye-tracking data can be used for the automatic detection of high-functioning autism in adults and that visual processing differences between the two groups exist when processing web pages.en
dc.formatapplication/pdfen
dc.languageeng
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttps://ieeexplore.ieee.org/document/9082703en
dc.subjectautismen
dc.subjecteye trackingen
dc.subjectWeben
dc.subjectscreeningen
dc.subjectdiagnostic classificationen
dc.subjectdetectionen
dc.subjectgaze trackingen
dc.subjectweb pagesen
dc.subjectdata collectionen
dc.subjectreliabilityen
dc.titleDetecting high-functioning autism in adults using eye tracking and machine learningen
dc.typeJournal articleen
dc.identifier.eissn1558-0210
dc.identifier.journalIEEE Transactions on Neural Systems and Rehabilitation Engineeringen
dc.date.updated2020-06-03T18:14:51Z
pubs.place-of-publicationUnited States
dc.date.accepted2020-04-27
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW09062020VYen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2020-06-09en
dc.source.volume28
dc.source.issue6
dc.source.beginpage1254
dc.source.endpage1261
dc.description.versionPublished version
refterms.dateFCD2020-06-09T10:27:58Z
refterms.versionFCDAM
refterms.dateFOA2020-06-09T10:31:17Z


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