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Détail de l'auteur
Auteur Jiwei WEI |
Documents disponibles écrits par cet auteur (1)
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Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach / Zhong ZHAO in Journal of Autism and Developmental Disorders, 53-3 (March 2023)
[article]
Titre : Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach Type de document : Texte imprimé et/ou numérique Auteurs : Zhong ZHAO, Auteur ; Jiwei WEI, Auteur ; Jiayi XING, Auteur ; Xiaobin ZHANG, Auteur ; Xingda QU, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur Article en page(s) : p.934-946 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : This study segmented the time series of gaze behavior from nineteen children with autism spectrum disorder (ASD) and 20 children with typical development in a face-to-face conversation. A machine learning approach showed that behavior segments produced by these two groups of participants could be classified with the highest accuracy of 74.15%. These results were further used to classify children using a threshold classifier. A maximum classification accuracy of 87.18% was achieved, under the condition that a participant was considered as 'ASD' if over 46% of the child?s 7-s behavior segments were classified as ASD-like behaviors. The idea of combining the behavior segmentation technique and the threshold classifier could maximally preserve participants' data, and promote the automatic screening of ASD. En ligne : https://doi.org/10.1007/s10803-022-05685-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=500
in Journal of Autism and Developmental Disorders > 53-3 (March 2023) . - p.934-946[article] Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach [Texte imprimé et/ou numérique] / Zhong ZHAO, Auteur ; Jiwei WEI, Auteur ; Jiayi XING, Auteur ; Xiaobin ZHANG, Auteur ; Xingda QU, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur . - p.934-946.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 53-3 (March 2023) . - p.934-946
Index. décimale : PER Périodiques Résumé : This study segmented the time series of gaze behavior from nineteen children with autism spectrum disorder (ASD) and 20 children with typical development in a face-to-face conversation. A machine learning approach showed that behavior segments produced by these two groups of participants could be classified with the highest accuracy of 74.15%. These results were further used to classify children using a threshold classifier. A maximum classification accuracy of 87.18% was achieved, under the condition that a participant was considered as 'ASD' if over 46% of the child?s 7-s behavior segments were classified as ASD-like behaviors. The idea of combining the behavior segmentation technique and the threshold classifier could maximally preserve participants' data, and promote the automatic screening of ASD. En ligne : https://doi.org/10.1007/s10803-022-05685-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=500