Characteristics of Visual Fixation in Chinese Children with Autism During Face-to-Face Conversations / Haiming TANG ; Xiaobin ZHANG ; Zhipeng ZHU ; Jiayi XING ; Wenzhou LI ; Da TAO ; Xingda QU ; Jianping LU in Journal of Autism and Developmental Disorders, 53-2 (February 2023)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 53-2 (February 2023) . - p.746-758 Titre : Characteristics of Visual Fixation in Chinese Children with Autism During Face-to-Face Conversations Type de document : Texte imprimé et/ou numérique Auteurs : Haiming TANG, Auteur ; Xiaobin ZHANG, Auteur ; Zhipeng ZHU, Auteur ; Jiayi XING, Auteur ; Wenzhou LI, Auteur ; Da TAO, Auteur ; Xingda QU, Auteur ; Jianping LU, Auteur Article en page(s) : p.746-758 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Few eye tracking studies have examined how people with autism spectrum disorder (ASD) visually attend during live interpersonal interaction, and none with the Chinese population. This study used an eye tracker to record the gaze behavior in 20 Chinese children with ASD and 23 children with typical development (TD) when they were engaged in a structured conversation. Results demonstrated that children with ASD looked significantly less at the interlocutor's mouth and whole-face, and more at background. Additionally, gaze behavior was found to vary with the conversational topic. Given the great variability in eye tracking findings in existing literature, future explorations might consider investigating how fundamental factors (i.e., participant's characteristics, tasks, and context) influence the gaze behavior in people with ASD. En ligne : https://doi.org/10.1007/s10803-021-04985-y Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=495 [article] Characteristics of Visual Fixation in Chinese Children with Autism During Face-to-Face Conversations [Texte imprimé et/ou numérique] / Haiming TANG, Auteur ; Xiaobin ZHANG, Auteur ; Zhipeng ZHU, Auteur ; Jiayi XING, Auteur ; Wenzhou LI, Auteur ; Da TAO, Auteur ; Xingda QU, Auteur ; Jianping LU, Auteur . - p.746-758. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 53-2 (February 2023) . - p.746-758 Index. décimale : PER Périodiques Résumé : Few eye tracking studies have examined how people with autism spectrum disorder (ASD) visually attend during live interpersonal interaction, and none with the Chinese population. This study used an eye tracker to record the gaze behavior in 20 Chinese children with ASD and 23 children with typical development (TD) when they were engaged in a structured conversation. Results demonstrated that children with ASD looked significantly less at the interlocutor's mouth and whole-face, and more at background. Additionally, gaze behavior was found to vary with the conversational topic. Given the great variability in eye tracking findings in existing literature, future explorations might consider investigating how fundamental factors (i.e., participant's characteristics, tasks, and context) influence the gaze behavior in people with ASD. En ligne : https://doi.org/10.1007/s10803-021-04985-y Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=495

Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms / Zhong ZHAO in Journal of Autism and Developmental Disorders, 52-7 (July 2022)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 52-7 (July 2022) . - p.3038-3049 Titre : Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms Type de document : Texte imprimé et/ou numérique Auteurs : Zhong ZHAO, Auteur ; Zhipeng ZHU, Auteur ; Xiaobin ZHANG, Auteur ; Haiming TANG, Auteur ; Jiayi XING, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur ; Xingda QU, Auteur Article en page(s) : p.3038-3049 Langues : Anglais (eng) Mots-clés : Algorithms  Autism Spectrum Disorder/diagnosis  Autistic Disorder  Child  Head Movements  Humans  Machine Learning  Autism  Biomarkers  Diagnosis  Head movement Index. décimale : PER Périodiques Résumé : Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes-no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD. En ligne : http://dx.doi.org/10.1007/s10803-021-05179-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477 [article] Identifying Autism with Head Movement Features by Implementing Machine Learning Algorithms [Texte imprimé et/ou numérique] / Zhong ZHAO, Auteur ; Zhipeng ZHU, Auteur ; Xiaobin ZHANG, Auteur ; Haiming TANG, Auteur ; Jiayi XING, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur ; Xingda QU, Auteur . - p.3038-3049. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 52-7 (July 2022) . - p.3038-3049 Mots-clés : Algorithms  Autism Spectrum Disorder/diagnosis  Autistic Disorder  Child  Head Movements  Humans  Machine Learning  Autism  Biomarkers  Diagnosis  Head movement Index. décimale : PER Périodiques Résumé : Our study investigated the feasibility of using head movement features to identify individuals with autism spectrum disorder (ASD). Children with ASD and typical development (TD) were required to answer ten yes-no questions, and they were encouraged to nod/shake head while doing so. The head rotation range (RR) and the amount of rotation per minute (ARPM) in the pitch (head nodding direction), yaw (head shaking direction) and roll (lateral head inclination) directions were computed, and further fed into machine learning classifiers as the input features. The maximum classification accuracy of 92.11% was achieved with the decision tree classifier with two features (i.e., RR_Pitch and ARPM_Yaw). Our study suggests that head movement dynamics contain objective biomarkers that could identify ASD. En ligne : http://dx.doi.org/10.1007/s10803-021-05179-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477

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