Autism Digital Phenotyping in Preschool- and School-Age Children / Kimberly L.H. CARPENTER ; Pradeep Raj Krishnappa BABU ; J. Matias DI MARTINO ; Steven ESPINOSA ; Scott N. COMPTON ; Naomi DAVIS ; Lauren FRANZ ; Marina SPANOS ; Guillermo SAPIRO ; Geraldine DAWSON in Autism Research, 18-6 (June 2025)  

Public ISBD [article]  inAutism Research > 18-6 (June 2025) . - p.1217-1233 Titre : Autism Digital Phenotyping in Preschool- and School-Age Children Type de document : texte imprimé Auteurs : Kimberly L.H. CARPENTER, Auteur ; Pradeep Raj Krishnappa BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Steven ESPINOSA, Auteur ; Scott N. COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Marina SPANOS, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur Article en page(s) : p.1217-1233 Langues : Anglais (eng) Mots-clés : autism  computer vision  digital phenotyping  preschool- and school-age Index. décimale : PER Périodiques Résumé : ABSTRACT There is a critical need for scalable and objective tools for autism screening and outcome monitoring, which can be used alongside traditional caregiver and clinical measures. To address this need, we developed SenseToKnow, a tablet- or smartphone-based digital phenotyping application (app), which uses computer vision and touch data to measure several autism-related behavioral features, such as social attention, facial and head movements, and visual-motor skills. Our previous work demonstrated that the SenseToKnow app can accurately detect and quantify behavioral signs of autism in 18 40-month-old toddlers. In the present study, we administered the SenseToKnow app on an iPad to 149 preschool- and school-age children (45 neurotypical and 104 autistic) between 3 and 8 years of age. Results revealed significant group differences between autistic and neurotypical children in terms of several behavioral features, which remained after controlling for sex and age. Repeat administration with a subgroup demonstrated stability in the individual digital phenotypes. Examining correlations between the Vineland Adaptive Behavior Scales and individual digital phenotypes, we found that autistic children with higher levels of communication, daily living, socialization, motor, and adaptive skills exhibited higher levels of social attention and coordinated gaze with speech, less frequent head movements, higher complexity of facial movements, higher overall attention, lower blink rates, and higher visual motor skills, demonstrating convergent validity between app features and clinical measures. App features were also significantly correlated with ratings on the Social Responsiveness Scale. These results suggest that the SenseToKnow app can be used as an accessible, scalable, and objective digital tool to measure autism-related behaviors in preschool- and school-age children. En ligne : https://doi.org/10.1002/aur.70032 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=558 [article] Autism Digital Phenotyping in Preschool- and School-Age Children [texte imprimé] / Kimberly L.H. CARPENTER, Auteur ; Pradeep Raj Krishnappa BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Steven ESPINOSA, Auteur ; Scott N. COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Marina SPANOS, Auteur ; Guillermo SAPIRO, Auteur ; Geraldine DAWSON, Auteur . - p.1217-1233. Langues : Anglais (eng) in Autism Research > 18-6 (June 2025) . - p.1217-1233 Mots-clés : autism  computer vision  digital phenotyping  preschool- and school-age Index. décimale : PER Périodiques Résumé : ABSTRACT There is a critical need for scalable and objective tools for autism screening and outcome monitoring, which can be used alongside traditional caregiver and clinical measures. To address this need, we developed SenseToKnow, a tablet- or smartphone-based digital phenotyping application (app), which uses computer vision and touch data to measure several autism-related behavioral features, such as social attention, facial and head movements, and visual-motor skills. Our previous work demonstrated that the SenseToKnow app can accurately detect and quantify behavioral signs of autism in 18 40-month-old toddlers. In the present study, we administered the SenseToKnow app on an iPad to 149 preschool- and school-age children (45 neurotypical and 104 autistic) between 3 and 8 years of age. Results revealed significant group differences between autistic and neurotypical children in terms of several behavioral features, which remained after controlling for sex and age. Repeat administration with a subgroup demonstrated stability in the individual digital phenotypes. Examining correlations between the Vineland Adaptive Behavior Scales and individual digital phenotypes, we found that autistic children with higher levels of communication, daily living, socialization, motor, and adaptive skills exhibited higher levels of social attention and coordinated gaze with speech, less frequent head movements, higher complexity of facial movements, higher overall attention, lower blink rates, and higher visual motor skills, demonstrating convergent validity between app features and clinical measures. App features were also significantly correlated with ratings on the Social Responsiveness Scale. These results suggest that the SenseToKnow app can be used as an accessible, scalable, and objective digital tool to measure autism-related behaviors in preschool- and school-age children. En ligne : https://doi.org/10.1002/aur.70032 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=558

Complexity analysis of head movements in autistic toddlers / Pradeep Raj KRISHNAPPA BABU in Journal of Child Psychology and Psychiatry, 64-1 (January 2023)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 64-1 (January 2023) . - p.156-166 Titre : Complexity analysis of head movements in autistic toddlers Type de document : texte imprimé Auteurs : Pradeep Raj KRISHNAPPA BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Zhuoqing CHANG, Auteur ; Sam PEROCHON, Auteur ; Rachel AIELLO, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Scott N. COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Steven ESPINOSA, Auteur ; Jacqueline FLOWERS, Auteur ; Geraldine DAWSON, Auteur ; Guillermo SAPIRO, Auteur Article en page(s) : p.156-166 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Background Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet. Methods Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy. Results Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers. Conclusions Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism. En ligne : https://doi.org/10.1111/jcpp.13681 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490 [article] Complexity analysis of head movements in autistic toddlers [texte imprimé] / Pradeep Raj KRISHNAPPA BABU, Auteur ; J. Matias DI MARTINO, Auteur ; Zhuoqing CHANG, Auteur ; Sam PEROCHON, Auteur ; Rachel AIELLO, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Scott N. COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Steven ESPINOSA, Auteur ; Jacqueline FLOWERS, Auteur ; Geraldine DAWSON, Auteur ; Guillermo SAPIRO, Auteur . - p.156-166. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 64-1 (January 2023) . - p.156-166 Index. décimale : PER Périodiques Résumé : Background Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet. Methods Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy. Results Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers. Conclusions Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism. En ligne : https://doi.org/10.1111/jcpp.13681 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490

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 Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders: A Preliminary Report / Dmitry Yu ISAEV in Journal of Autism and Developmental Disorders, 54-6 (June 2024)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 54-6 (June 2024) . - p.2286-2297 Titre : Computer Vision Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders: A Preliminary Report Type de document : texte imprimé Auteurs : Dmitry Yu ISAEV, Auteur ; Maura SABATOS-DEVITO, Auteur ; J. Matias DI MARTINO, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Rachel AIELLO, Auteur ; Scott N. COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Connor SULLIVAN, Auteur ; Geraldine DAWSON, Auteur ; Guillermo SAPIRO, Auteur Article en page(s) : p.2286-2297 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : We report preliminary results of computer vision analysis of caregiver-child interactions during free play with children diagnosed with autism (N = 29, 41-91 months), attention-deficit/hyperactivity disorder (ADHD, N = 22, 48-100 months), or combined autism+ADHD (N = 20, 56-98 months), and neurotypical children (NT, N = 7, 55-95 months). We conducted micro-analytic analysis of 'reaching to a toy,' as a proxy for initiating or responding to a toy play bout. Dyadic analysis revealed two clusters of interaction patterns, which differed in frequency of 'reaching to a toy' and caregivers' contingent responding to the child?s reach for a toy by also reaching for a toy. Children in dyads with higher caregiver responsiveness had less developed language, communication, and socialization skills. Clusters were not associated with diagnostic groups. These results hold promise for automated methods of characterizing caregiver responsiveness in dyadic interactions for assessment and outcome monitoring in clinical trials. En ligne : https://doi.org/10.1007/s10803-023-05973-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=530 [article] Computer Vision Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders: A Preliminary Report [texte imprimé] / Dmitry Yu ISAEV, Auteur ; Maura SABATOS-DEVITO, Auteur ; J. Matias DI MARTINO, Auteur ; Kimberly L.H. CARPENTER, Auteur ; Rachel AIELLO, Auteur ; Scott N. COMPTON, Auteur ; Naomi DAVIS, Auteur ; Lauren FRANZ, Auteur ; Connor SULLIVAN, Auteur ; Geraldine DAWSON, Auteur ; Guillermo SAPIRO, Auteur . - p.2286-2297. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 54-6 (June 2024) . - p.2286-2297 Index. décimale : PER Périodiques Résumé : We report preliminary results of computer vision analysis of caregiver-child interactions during free play with children diagnosed with autism (N = 29, 41-91 months), attention-deficit/hyperactivity disorder (ADHD, N = 22, 48-100 months), or combined autism+ADHD (N = 20, 56-98 months), and neurotypical children (NT, N = 7, 55-95 months). We conducted micro-analytic analysis of 'reaching to a toy,' as a proxy for initiating or responding to a toy play bout. Dyadic analysis revealed two clusters of interaction patterns, which differed in frequency of 'reaching to a toy' and caregivers' contingent responding to the child?s reach for a toy by also reaching for a toy. Children in dyads with higher caregiver responsiveness had less developed language, communication, and socialization skills. Clusters were not associated with diagnostic groups. These results hold promise for automated methods of characterizing caregiver responsiveness in dyadic interactions for assessment and outcome monitoring in clinical trials. En ligne : https://doi.org/10.1007/s10803-023-05973-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=530

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

Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism / Kimberly L.H. CARPENTER in Autism Research, 14-3 (March 2021)  

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Impact of a digital Modified Checklist for Autism in Toddlers-Revised on likelihood and age of autism diagnosis and referral for developmental evaluation / Samantha MAJOR in Autism, 24-7 (October 2020)  

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Relationship between quantitative digital behavioral features and clinical profiles in young autistic children / Marika C. COFFMAN in Autism Research, 16-7 (July 2023)  

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A scalable computational approach to assessing response to name in toddlers with autism / Sam PEROCHON in Journal of Child Psychology and Psychiatry, 62-9 (September 2021)  

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

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