dc.contributor.author | Fernández Lanvin, Daniel | |
dc.contributor.author | González Rodríguez, Bernardo Martín | |
dc.contributor.author | Andrés Suárez, Javier | |
dc.contributor.author | Camero, Raquel | |
dc.date.accessioned | 2023-12-13T08:31:38Z | |
dc.date.available | 2023-12-13T08:31:38Z | |
dc.date.issued | 2023-12-11 | |
dc.identifier.citation | Multimedia Tools and Applications (2023); doi:10.1007/s11042-023-17694-8 | |
dc.identifier.issn | 1380-7501 | |
dc.identifier.uri | https://hdl.handle.net/10651/70462 | |
dc.description.abstract | According to official estimations, autism spectrum disorder (ASD) affects around 1%
of European newborns. The high level of dependency of ASD-affected subjects entails
an extremely high social and economic cost. However, early intervention can drastically
improve children’s development and thus reduce their dependency. One of the main common
characteristics of subjects with ASD is difficulties with social interaction, which
determines how they react to certain stimuli. This behavior can be automatically detected
by analyzing their gaze. This study explores and evaluates the feasibility of automatic
screening for ASD in toddlers under 24 months of age based on this specific behavior.
We applied a matched pairs experimental design and a set of test videos, using a set of
variables extracted from gaze analysis from toddlers using eye-tracking devices. The different
videos try to capture social engagement, social information gathering gaze exchanges,
and gaze following. We used the data to make a thorough comparison of machine learning
algorithms (nine learning schemes), including some that were used in related prior
research, and others that are popular in classification problems. The results show that several
of the tested algorithms provided notable performance. | spa |
dc.description.sponsorship | This work was partially funded by the Department of Science, Innovation and Universities (Spain) under the National Program for Research, Development, and Innovation (Project
RTI2018-099235-B-I00) and by the Fundación Trapote (Ayuntamiento de Gijón) | |
dc.language.iso | eng | spa |
dc.relation.ispartof | Multimedia Tools and Applications | spa |
dc.rights | CC Reconocimiento 4.0 Internacional | |
dc.rights | © The Author(s) 2023 | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.title | Towards an automatic early screening system for autism spectrum disorder in toddlers based on eye‑tracking | spa |
dc.type | journal article | spa |
dc.identifier.doi | 10.1007/s11042-023-17694-8 | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099235-B-I00/ES/MODELADO DE USUARIO PARA PERSONALIZACION DE INTERFAZ GUIADO POR ANALISIS AUTOMATICO DE PATRONES DE COMPORTAMIENTO/ | spa |
dc.relation.publisherversion | https://doi.org/10.1007/s11042-023-17694-8 | |
dc.rights.accessRights | open access | spa |
dc.type.hasVersion | VoR | |