Autism Spectrum Disorder diagnosis based on trajectories of eye tracking data

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dc.contributorSistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSP
dc.contributor.authorV, Thiago Cardoso
dc.contributor.authorMICHELASSI, Gabriel C.
dc.contributor.authorFRANCO, Felipe O.
dc.contributor.authorSUMIYA, Fernando M.
dc.contributor.authorPORTOLESE, Joana
dc.contributor.authorBRENTANI, Helena
dc.contributor.authorMACHADO-LIMA, Ariane
dc.contributor.authorNUNES, Fatima L. S.
dc.date.accessioned2022-11-25T13:47:10Z
dc.date.available2022-11-25T13:47:10Z
dc.date.issued2021
dc.description.abstractThe use of Eye Tracking (ET) has been investigated as an auxiliary mechanism to diagnose Autism Spectrum Disorder (ASD). One of the paradigms investigated using ET is Joint Attention (JA), which refers to moments when two individuals are focused on the same object/event so that both are aware that the focus of attention is shared. The computational tools that assist in the diagnosis of ASD have used Image Processing and Machine Learning techniques to process images, videos and ET signals. However, the JA paradigm is still little explored and presents challenges, as it requires analyzing the gaze trajectory and needs innovative approaches. The purpose of this article is to propose a model capable of extracting features from a video used as a stimulus to capture ET signals in order to verify JA and classify individuals as belonging to the ASD or Typical Development (TD) group. The main differential in relation to the approaches in the literature is the definition and implementation of the concept of floating Regions of Interest, which allows monitoring the gaze in relation to an object, considering its semantics, even if the object presents different characteristics throughout the video. A model based on ensembles of Random Forest classifiers was implemented to classify individuals as ASD or TD using the trajectory features extracted from the ET signals. The method reached 0.75 accuracy and 0.82 F1-score, indicating that the proposed approach, based on trajectory and JA, has the potential to be applied to assist in the diagnosis of ASD.eng
dc.description.conferencedateJUN 07-09, 2021
dc.description.conferencelocalELECTR NETWORK
dc.description.conferencename34th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS)
dc.description.indexPubMedeng
dc.description.sponsorshipCNPq [157535/20177, 309030/2019-6]
dc.description.sponsorshipFAPESP/INCT-MACC [2014/50889-7]
dc.description.sponsorshipFAPESP [2020/01992-0]
dc.description.sponsorshipCAPES
dc.description.sponsorshipPRONAS/PCD [25000.002484/2017-17]
dc.identifier.citation2021 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), p.50-55, 2021
dc.identifier.doi10.1109/CBMS52027.2021.00016
dc.identifier.isbn978-1-6654-4121-6
dc.identifier.issn2372-9198
dc.identifier.urihttps://observatorio.fm.usp.br/handle/OPI/50272
dc.language.isoeng
dc.publisherIEEEeng
dc.relation.ispartof2021 Ieee 34th International Symposium on Computer-Based Medical Systems (cbms)
dc.relation.ispartofseriesIEEE International Symposium on Computer-Based Medical Systems
dc.rightsrestrictedAccesseng
dc.rights.holderCopyright IEEEeng
dc.subjectAutism Spectrum Disordereng
dc.subjectEnsemble Methodeng
dc.subjectEye trackingeng
dc.subjectImage processingeng
dc.subjectJoint attentioneng
dc.subjectMachine Learningeng
dc.subjectRandom Foresteng
dc.subjectTrajectoryeng
dc.subjectTypical Developmenteng
dc.subject.wosComputer Science, Interdisciplinary Applicationseng
dc.subject.wosEngineering, Biomedicaleng
dc.subject.wosMedical Informaticseng
dc.titleAutism Spectrum Disorder diagnosis based on trajectories of eye tracking dataeng
dc.typeconferenceObjecteng
dc.type.categoryproceedings papereng
dc.type.versionpublishedVersioneng
dspace.entity.typePublication
hcfmusp.affiliation.countryEstados Unidos
hcfmusp.affiliation.countryisous
hcfmusp.author.externalV, Thiago Cardoso:Univ Sao Paulo, Sch Arts Sci & Humanities EACH, Sao Paulo, SP, Brazil
hcfmusp.author.externalMICHELASSI, Gabriel C.:Univ Sao Paulo, Sch Arts Sci & Humanities EACH, Sao Paulo, SP, Brazil
hcfmusp.author.externalFRANCO, Felipe O.:USP, Interunit PostGrad Program Bioinformat, Inst Math Stat IME, Rockville, MD USA
hcfmusp.author.externalMACHADO-LIMA, Ariane:Univ Sao Paulo, Sch Arts Sci & Humanities EACH, Sao Paulo, SP, Brazil
hcfmusp.author.externalNUNES, Fatima L. S.:Univ Sao Paulo, Sch Arts Sci & Humanities EACH, Sao Paulo, SP, Brazil
hcfmusp.citation.scopus4
hcfmusp.contributor.author-fmusphcFERNANDO MITSUO SUMIYA
hcfmusp.contributor.author-fmusphcJOANA TEIXEIRA PORTOLESE PASCALICCHIO
hcfmusp.contributor.author-fmusphcHELENA PAULA BRENTANI
hcfmusp.description.beginpage50
hcfmusp.description.endpage55
hcfmusp.origemWOS
hcfmusp.origem.scopus2-s2.0-85110930990
hcfmusp.origem.wosWOS:000847341000009
hcfmusp.publisher.cityNEW YORKeng
hcfmusp.publisher.countryUSAeng
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hcfmusp.scopus.lastupdate2024-05-17
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relation.isAuthorOfPublication302b2020-cbc3-4a79-8697-ece1308a4bdc
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