{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T18:59:56Z","timestamp":1791658796487,"version":"4.3.4"},"reference-count":120,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,4,26]],"date-time":"2020-04-26T00:00:00Z","timestamp":1587859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Economy, Industry, and Competitiveness","award":["IDI-20170912"],"award-info":[{"award-number":["IDI-20170912"]}]},{"DOI":"10.13039\/501100003359","name":"Generalitat Valenciana","doi-asserted-by":"publisher","award":["PROMETEU\/2019\/105"],"award-info":[{"award-number":["PROMETEU\/2019\/105"]}],"id":[{"id":"10.13039\/501100003359","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Union through the Operational Program of the European Regional development Fund (ERDF) of the Valencian Community 2014-2020","award":["IDIFEDER\/2018\/029"],"award-info":[{"award-number":["IDIFEDER\/2018\/029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>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\u2019 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\u2019 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\u2019 biomarkers that could contribute to improving ASD diagnosis.<\/jats:p>","DOI":"10.3390\/jcm9051260","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T01:29:15Z","timestamp":1588123755000},"page":"1260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Machine Learning and Virtual Reality on Body Movements\u2019 Behaviors to Classify Children with Autism Spectrum Disorder"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9207-0636","authenticated-orcid":false,"given":"Mariano","family":"Alca\u00f1iz Raya","sequence":"first","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda (i3B), Universitat Polit\u00e9cnica de Valencia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1271-2892","authenticated-orcid":false,"given":"Javier","family":"Mar\u00edn-Morales","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda (i3B), Universitat Polit\u00e9cnica de Valencia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Eleonora","family":"Minissi","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda (i3B), Universitat Polit\u00e9cnica de Valencia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gonzalo","family":"Teruel Garcia","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda (i3B), Universitat Polit\u00e9cnica de Valencia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Abad","sequence":"additional","affiliation":[{"name":"Red Cenit, Centros de Desarrollo Cognitivo, 46020 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2577-0039","authenticated-orcid":false,"given":"Irene Alice","family":"Chicchi Giglioli","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda (i3B), Universitat Polit\u00e9cnica de Valencia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association (2013). 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