Computer vision analysis captures atypical attention in toddlers with autism / K. CAMPBELL in Autism, 23-3 (April 2019)  

Public ISBD [article]  inAutism > 23-3 (April 2019) . - p.619-628 Titre : Computer vision analysis captures atypical attention in toddlers with autism Type de document : Texte imprimé et/ou numérique Auteurs : K. CAMPBELL, Auteur ; Kimberly L. H. CARPENTER, Auteur ; J. HASHEMI, Auteur ; S. ESPINOSA, Auteur ; S. MARSAN, Auteur ; J. S. BORG, Auteur ; Z. CHANG, Auteur ; Q. QIU, Auteur ; S. VERMEER, Auteur ; E. ADLER, Auteur ; M. TEPPER, Auteur ; H. L. EGGER, Auteur ; J. P. BAKER, Auteur ; G. SAPIRO, Auteur ; G. DAWSON, Auteur Article en page(s) : p.619-628 Langues : Anglais (eng) Mots-clés : autism spectrum disorders  behavioral measurement  development  pre-school children  social cognition and social behavior Index. décimale : PER Périodiques Résumé : To demonstrate the capability of computer vision analysis to detect atypical orienting and attention behaviors in toddlers with autism spectrum disorder. One hundered and four toddlers of 16-31 months old (mean = 22) participated in this study. Twenty-two of the toddlers had autism spectrum disorder and 82 had typical development or developmental delay. Toddlers watched video stimuli on a tablet while the built-in camera recorded their head movement. Computer vision analysis measured participants' attention and orienting in response to name calls. Reliability of the computer vision analysis algorithm was tested against a human rater. Differences in behavior were analyzed between the autism spectrum disorder group and the comparison group. Reliability between computer vision analysis and human coding for orienting to name was excellent (intra-class coefficient 0.84, 95% confidence interval 0.67-0.91). Only 8% of toddlers with autism spectrum disorder oriented to name calling on >1 trial, compared to 63% of toddlers in the comparison group (p = 0.002). Mean latency to orient was significantly longer for toddlers with autism spectrum disorder (2.02 vs 1.06 s, p = 0.04). Sensitivity for autism spectrum disorder of atypical orienting was 96% and specificity was 38%. Older toddlers with autism spectrum disorder showed less attention to the videos overall (p = 0.03). Automated coding offers a reliable, quantitative method for detecting atypical social orienting and reduced sustained attention in toddlers with autism spectrum disorder. En ligne : http://dx.doi.org/10.1177/1362361318766247 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=392 [article] Computer vision analysis captures atypical attention in toddlers with autism [Texte imprimé et/ou numérique] / K. CAMPBELL, Auteur ; Kimberly L. H. CARPENTER, Auteur ; J. HASHEMI, Auteur ; S. ESPINOSA, Auteur ; S. MARSAN, Auteur ; J. S. BORG, Auteur ; Z. CHANG, Auteur ; Q. QIU, Auteur ; S. VERMEER, Auteur ; E. ADLER, Auteur ; M. TEPPER, Auteur ; H. L. EGGER, Auteur ; J. P. BAKER, Auteur ; G. SAPIRO, Auteur ; G. DAWSON, Auteur . - p.619-628. Langues : Anglais (eng) in Autism > 23-3 (April 2019) . - p.619-628 Mots-clés : autism spectrum disorders  behavioral measurement  development  pre-school children  social cognition and social behavior Index. décimale : PER Périodiques Résumé : To demonstrate the capability of computer vision analysis to detect atypical orienting and attention behaviors in toddlers with autism spectrum disorder. One hundered and four toddlers of 16-31 months old (mean = 22) participated in this study. Twenty-two of the toddlers had autism spectrum disorder and 82 had typical development or developmental delay. Toddlers watched video stimuli on a tablet while the built-in camera recorded their head movement. Computer vision analysis measured participants' attention and orienting in response to name calls. Reliability of the computer vision analysis algorithm was tested against a human rater. Differences in behavior were analyzed between the autism spectrum disorder group and the comparison group. Reliability between computer vision analysis and human coding for orienting to name was excellent (intra-class coefficient 0.84, 95% confidence interval 0.67-0.91). Only 8% of toddlers with autism spectrum disorder oriented to name calling on >1 trial, compared to 63% of toddlers in the comparison group (p = 0.002). Mean latency to orient was significantly longer for toddlers with autism spectrum disorder (2.02 vs 1.06 s, p = 0.04). Sensitivity for autism spectrum disorder of atypical orienting was 96% and specificity was 38%. Older toddlers with autism spectrum disorder showed less attention to the videos overall (p = 0.03). Automated coding offers a reliable, quantitative method for detecting atypical social orienting and reduced sustained attention in toddlers with autism spectrum disorder. En ligne : http://dx.doi.org/10.1177/1362361318766247 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=392

Computer vision tools for low-cost and noninvasive measurement of autism-related behaviors in infants / J. HASHEMI in Autism Research and Treatment, 2014 (2014)  

Public ISBD [article]  inAutism Research and Treatment > 2014 (2014) Titre : Computer vision tools for low-cost and noninvasive measurement of autism-related behaviors in infants Type de document : Texte imprimé et/ou numérique Auteurs : J. HASHEMI, Auteur ; M. TEPPER, Auteur ; T. VALLIN SPINA, Auteur ; A. ESLER, Auteur ; V. MORELLAS, Auteur ; N. PAPANIKOLOPOULOS, Auteur ; H. EGGER, Auteur ; G. DAWSON, Auteur ; G. SAPIRO, Auteur Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated which promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests that behavioral signs can be observed late in the first year of life. Many of these studies involve extensive frame-by-frame video observation and analysis of a child's natural behavior. Although nonintrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are burdensome for clinical and large population research purposes. This work is a first milestone in a long-term project on non-invasive early observation of children in order to aid in risk detection and research of neurodevelopmental disorders. We focus on providing low-cost computer vision tools to measure and identify ASD behavioral signs based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure responses to general ASD risk assessment tasks and activities outlined by the AOSI which assess visual attention by tracking facial features. We show results, including comparisons with expert and nonexpert clinicians, which demonstrate that the proposed computer vision tools can capture critical behavioral observations and potentially augment the clinician's behavioral observations obtained from real in-clinic assessments. En ligne : http://dx.doi.org/10.1155/2014/935686 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=332 [article] Computer vision tools for low-cost and noninvasive measurement of autism-related behaviors in infants [Texte imprimé et/ou numérique] / J. HASHEMI, Auteur ; M. TEPPER, Auteur ; T. VALLIN SPINA, Auteur ; A. ESLER, Auteur ; V. MORELLAS, Auteur ; N. PAPANIKOLOPOULOS, Auteur ; H. EGGER, Auteur ; G. DAWSON, Auteur ; G. SAPIRO, Auteur. Langues : Anglais (eng) in Autism Research and Treatment > 2014 (2014) Index. décimale : PER Périodiques Résumé : The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated which promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests that behavioral signs can be observed late in the first year of life. Many of these studies involve extensive frame-by-frame video observation and analysis of a child's natural behavior. Although nonintrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are burdensome for clinical and large population research purposes. This work is a first milestone in a long-term project on non-invasive early observation of children in order to aid in risk detection and research of neurodevelopmental disorders. We focus on providing low-cost computer vision tools to measure and identify ASD behavioral signs based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure responses to general ASD risk assessment tasks and activities outlined by the AOSI which assess visual attention by tracking facial features. We show results, including comparisons with expert and nonexpert clinicians, which demonstrate that the proposed computer vision tools can capture critical behavioral observations and potentially augment the clinician's behavioral observations obtained from real in-clinic assessments. En ligne : http://dx.doi.org/10.1155/2014/935686 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=332

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