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Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder

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Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder

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dc.contributor.author Alcañiz Raya, Mariano Luis es_ES
dc.contributor.author Marín-Morales, Javier es_ES
dc.contributor.author Minissi, Maria Eleonora es_ES
dc.contributor.author Teruel Garcia, Gonzalo es_ES
dc.contributor.author Abad, Luis es_ES
dc.contributor.author CHICCHI-GIGLIOLI, IRENE ALICE es_ES
dc.date.accessioned 2021-07-22T03:33:37Z
dc.date.available 2021-07-22T03:33:37Z
dc.date.issued 2020-05 es_ES
dc.identifier.uri http://hdl.handle.net/10251/169737
dc.description.abstract [EN] Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements' frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients' subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements' biomarkers that could contribute to improving ASD diagnosis. es_ES
dc.description.sponsorship This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness funded project "Immersive virtual environment for the evaluation and training of children with autism spectrum disorder: T Room" (IDI-20170912) and by the Generalitat Valenciana funded project REBRAND (PROMETEO/2019/105). Furthermore, this work was co-founded by the European Union through the Operational Program of the European Regional development Fund (ERDF) of the Valencian Community 2014-2020 (IDIFEDER/2018/029). es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Journal of Clinical Medicine es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Autism spectrum disorder es_ES
dc.subject Body movements es_ES
dc.subject Repetitive behaviors es_ES
dc.subject Virtual reality es_ES
dc.subject Machine learning es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.title Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/jcm9051260 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//IDI-20170912/ES/Virtual Immersive Environment for the Assessment and Training of Autism Spectrum Disorder children (T-ROOM)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//IDIFEDER%2F2018%2F029/ES/INTERFACES DE REALIDAD MIXTA APLICADA A SALUD Y TOMA DE DECISIONES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F105/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica es_ES
dc.description.bibliographicCitation Alcañiz Raya, ML.; Marín-Morales, J.; Minissi, ME.; Teruel Garcia, G.; Abad, L.; Chicchi-Giglioli, IA. (2020). Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder. Journal of Clinical Medicine. 9(5):1-20. https://doi.org/10.3390/jcm9051260 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/jcm9051260 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 20 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 5 es_ES
dc.identifier.eissn 2077-0383 es_ES
dc.identifier.pmid 32357517 es_ES
dc.identifier.pmcid PMC7287942 es_ES
dc.relation.pasarela S\409578 es_ES
dc.contributor.funder CEDECO RED CENIT S.L. es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía, Industria y Competitividad es_ES
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