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Computational modeling of psycho-physiological arousal and social initiation of children with autism in interventions through full-body interaction

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dc.contributor.author Sayis, Batuhan
dc.contributor.author Crowell, Ciera
dc.contributor.author Benitez, Juan Pablo
dc.contributor.author Ramírez, Rafael,1966-
dc.contributor.author Parés, Narcís, 1966-
dc.date.accessioned 2019-12-17T14:11:28Z
dc.date.available 2019-12-17T14:11:28Z
dc.date.issued 2019
dc.identifier.citation Sayis B, Crowell C, Benitez J, Ramirez R, Pares N. Computational modeling of psycho-physiological arousal and social initiation of children with autism in interventions through full-body interaction. In: 8th International Conference on Affective Computing and Intelligent Interaction (ACII); 2019 Sep 3-6; Cambridge, United Kingdom. New Jersey: IEEE; 2019. p. 573-9. DOI: 10.1109/ACII.2019.8925474
dc.identifier.isbn 978-1-7281-3888-6
dc.identifier.issn 2156-8111
dc.identifier.uri http://hdl.handle.net/10230/43191
dc.description Comunicació presentada a: 8th International Conference on Affective Computing and Intelligent Interaction (ACII) celebrat del 3 al 6 de setembre de 2019 a Cambridge, Regne Unit.
dc.description.abstract This study is part of a larger project that wants to foster social initiation behaviors in children with Autism Spectrum Disorder (ASD). We approach this through a full-body interactive Mixed Reality (MR) experience that mediates a face-to-face play session between an ASD child and a non-ASD child. The goal of this study is to obtain a data model that allows us to evaluate the goodness of the MR system compared to a typical social intervention strategy based on construction tools (in this case LEGO bricks) which acts as the control condition. In this paper we present our first analysis of the arousal generated by the MR experience compared to that generated in the control condition. We address this by analyzing psychophysiological data recorded during the social interaction behaviors in the ASD child while playing with the non-ASD child. We followed a repeated-measures design with two conditions: our full-body interaction MR environment and the typical social intervention strategy based on LEGO bricks. To measure physiology, Electrocardiogram (ECG), Electrodermal Activity (EDA) and Accelerometer (ACC) data were acquired through a wearable designed by our lab. We used machine learning techniques to analyze the huge amount of multimodal data from the ASD children obtained during 18 trials (3 female and 15 male). As a result, we were capable of classifying social initiation behaviors of ASD children during the MR environment sessions and those occurring during the LEGO construction sessions based on the psycho-physiological data sources. This is a first sign showing that our MR system has specific properties, compared to a traditional construction-based intervention, which potentially provide a new interesting context to intervention in ASD.
dc.description.sponsorship This work has been funded by Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Program (MDM-2015-0502).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof 8th International Conference on Affective Computing and Intelligent Interaction (ACII); 2019 Sep 3-6; Cambridge, United Kingdom. New Jersey: IEEE; 2019.
dc.rights © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/ACII.2019.8925474
dc.title Computational modeling of psycho-physiological arousal and social initiation of children with autism in interventions through full-body interaction
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.1109/ACII.2019.8925474
dc.subject.keyword Autism Spectrum Condition
dc.subject.keyword Mixed Reality
dc.subject.keyword Embodied Interaction
dc.subject.keyword Social Initiation
dc.subject.keyword Arousal
dc.subject.keyword Psychophysiology
dc.subject.keyword Computational Modeling
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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