Computer vision analysis captures atypical attention in toddlers with autism / Kathleen 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 Auteurs : Kathleen CAMPBELL, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Jordan HASHEMI, Auteur ; Steven ESPINOSA, Auteur ; Samuel MARSAN, Auteur ; Jana Schaich BORG, Auteur ; Zhuoqing CHANG, Auteur ; Qiang QIU, Auteur ; Saritha VERMEER, Auteur ; Elizabeth ADLER, Auteur ; Mariano TEPPER, Auteur ; Helen Link EGGER, Auteur ; Jeffery P. BAKER, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine 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] / Kathleen CAMPBELL, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Jordan HASHEMI, Auteur ; Steven ESPINOSA, Auteur ; Samuel MARSAN, Auteur ; Jana Schaich BORG, Auteur ; Zhuoqing CHANG, Auteur ; Qiang QIU, Auteur ; Saritha VERMEER, Auteur ; Elizabeth ADLER, Auteur ; Mariano TEPPER, Auteur ; Helen Link EGGER, Auteur ; Jeffery P. BAKER, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine 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 / Jordan 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 Auteurs : Jordan HASHEMI, Auteur ; Mariano TEPPER, Auteur ; T. VALLIN SPINA, Auteur ; Amy N. ESLER, Auteur ; V. MORELLAS, Auteur ; N. PAPANIKOLOPOULOS, Auteur ; H. EGGER, Auteur ; Geraldine DAWSON, Auteur ; Guillermo 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] / Jordan HASHEMI, Auteur ; Mariano TEPPER, Auteur ; T. VALLIN SPINA, Auteur ; Amy N. ESLER, Auteur ; V. MORELLAS, Auteur ; N. PAPANIKOLOPOULOS, Auteur ; H. EGGER, Auteur ; Geraldine DAWSON, Auteur ; Guillermo 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

A Six-Minute Measure of Vocalizations in Toddlers with Autism Spectrum Disorder / Elena J. TENENBAUM in Autism Research, 13-8 (August 2020)  

Public ISBD [article]  inAutism Research > 13-8 (August 2020) . - p.1373-1382 Titre : A Six-Minute Measure of Vocalizations in Toddlers with Autism Spectrum Disorder Type de document : texte imprimé Auteurs : Elena J. TENENBAUM, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Maura SABATOS-DEVITO, Auteur ; Jordan HASHEMI, Auteur ; Saritha VERMEER, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur Article en page(s) : p.1373-1382 Langues : Anglais (eng) Mots-clés : developmental psychology  early detection  early signs  infants  language Index. décimale : PER Périodiques Résumé : To improve early identification of autism spectrum disorder (ASD), we need objective, reliable, and accessible measures. To that end, a previous study demonstrated that a tablet-based application (app) that assessed several autism risk behaviors distinguished between toddlers with ASD and non-ASD toddlers. Using vocal data collected during this study, we investigated whether vocalizations uttered during administration of this app can distinguish among toddlers aged 16-31 months with typical development (TD), language or developmental delay (DLD), and ASD. Participant's visual and vocal responses were recorded using the camera and microphone in a tablet while toddlers watched movies designed to elicit behaviors associated with risk for ASD. Vocalizations were then coded offline. Results showed that (a) children with ASD and DLD were less likely to produce words during app administration than TD participants; (b) the ratio of syllabic vocalizations to all vocalizations was higher among TD than ASD or DLD participants; and (c) the rates of nonsyllabic vocalizations were higher in the ASD group than in either the TD or DLD groups. Those producing more nonsyllabic vocalizations were 24 times more likely to be diagnosed with ASD. These results lend support to previous findings that early vocalizations might be useful in identifying risk for ASD in toddlers and demonstrate the feasibility of using a scalable tablet-based app for assessing vocalizations in the context of a routine pediatric visit. LAY SUMMARY: Although parents often report symptoms of autism spectrum disorder (ASD) in infancy, we are not yet reliably diagnosing ASD until much later in development. A previous study tested a tablet-based application (app) that recorded behaviors we know are associated with ASD to help identify children at risk for the disorder. Here we measured how children vocalize while they watched the movies presented on the tablet. Children with ASD were less likely to produce words, less likely to produce speechlike sounds, and more likely to produce atypical sounds while watching these movies. These measures, combined with other behaviors measured by the app, might help identify which children should be evaluated for ASD. Autism Res 2020, 13: 1373-1382. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. En ligne : http://dx.doi.org/10.1002/aur.2293 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=430 [article] A Six-Minute Measure of Vocalizations in Toddlers with Autism Spectrum Disorder [texte imprimé] / Elena J. TENENBAUM, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Maura SABATOS-DEVITO, Auteur ; Jordan HASHEMI, Auteur ; Saritha VERMEER, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur . - p.1373-1382. Langues : Anglais (eng) in Autism Research > 13-8 (August 2020) . - p.1373-1382 Mots-clés : developmental psychology  early detection  early signs  infants  language Index. décimale : PER Périodiques Résumé : To improve early identification of autism spectrum disorder (ASD), we need objective, reliable, and accessible measures. To that end, a previous study demonstrated that a tablet-based application (app) that assessed several autism risk behaviors distinguished between toddlers with ASD and non-ASD toddlers. Using vocal data collected during this study, we investigated whether vocalizations uttered during administration of this app can distinguish among toddlers aged 16-31 months with typical development (TD), language or developmental delay (DLD), and ASD. Participant's visual and vocal responses were recorded using the camera and microphone in a tablet while toddlers watched movies designed to elicit behaviors associated with risk for ASD. Vocalizations were then coded offline. Results showed that (a) children with ASD and DLD were less likely to produce words during app administration than TD participants; (b) the ratio of syllabic vocalizations to all vocalizations was higher among TD than ASD or DLD participants; and (c) the rates of nonsyllabic vocalizations were higher in the ASD group than in either the TD or DLD groups. Those producing more nonsyllabic vocalizations were 24 times more likely to be diagnosed with ASD. These results lend support to previous findings that early vocalizations might be useful in identifying risk for ASD in toddlers and demonstrate the feasibility of using a scalable tablet-based app for assessing vocalizations in the context of a routine pediatric visit. LAY SUMMARY: Although parents often report symptoms of autism spectrum disorder (ASD) in infancy, we are not yet reliably diagnosing ASD until much later in development. A previous study tested a tablet-based application (app) that recorded behaviors we know are associated with ASD to help identify children at risk for the disorder. Here we measured how children vocalize while they watched the movies presented on the tablet. Children with ASD were less likely to produce words, less likely to produce speechlike sounds, and more likely to produce atypical sounds while watching these movies. These measures, combined with other behaviors measured by the app, might help identify which children should be evaluated for ASD. Autism Res 2020, 13: 1373-1382. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. En ligne : http://dx.doi.org/10.1002/aur.2293 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=430

---

1 (1 - 3 / 3) Par page : 25 50 100 200