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Engagement detection for children with autism spectrum disorder

Chorianopoulou Arodami, Tzinis Efthymios, Iosif Elias, Papoulidi Asimenia, Papailiou Christina, Potamianos Alexandros

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URIhttp://purl.tuc.gr/dl/dias/D1444391-5952-4B21-A0DA-6FEDBBD6413A-
Identifierhttps://ieeexplore.ieee.org/document/7953119/-
Identifierhttps://doi.org/10.1109/ICASSP.2017.7953119-
Languageen-
Extent5 pagesen
TitleEngagement detection for children with autism spectrum disorderen
CreatorChorianopoulou Arodamien
CreatorΧωριανοπουλου Αροδαμηel
CreatorTzinis Efthymiosen
CreatorIosif Eliasen
CreatorPapoulidi Asimeniaen
CreatorPapailiou Christinaen
CreatorPotamianos Alexandrosen
CreatorΠοταμιανος Αλεξανδροςel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryChildren with Autism Spectrum Disorder (ASD) face several difficulties in social communication. Hence, analyzing social interaction can provide insight on their social and cognitive skills. In this paper, we investigate the degree of engagement of children in interactions with their parents. Features derived from both participants including acoustic, linguistic and dialogue act features are explored. The effect of visual cues is also investigated. We experimented on the task of engagement detection using video-recorded sessions consisting of interactions of typically developing (TD) and ASD children. Results show that engagement is easier to predict for TD children than for ASD children, and that the parent's actions/movements are better predictors of the child's degree of engagement.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-05-08-
Date of Publication2017-
SubjectAutism spectrum disorderen
SubjectChild engagementen
SubjectEngagement detectionen
Bibliographic CitationA. Chorianopoulou, E.Tzinis, E. Iosif, A. Papoulidi, C. Papailiou and A. Potamianos, "Engagement detection for children with autism spectrum disorder," in IEEE International Conference on Acoustics, Speech and Signal Processing, 2017, pp. 5055-5059. doi: 10.1109/ICASSP.2017.7953119en

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