A scalable app for measuring autism risk behaviors in young children: A technical validity and feasibility study
In spite of recent advances in the genetics and neuroscience of early childhood mental health, behavioral observation is still the gold standard in screening, diagnosis, and outcome assessment. Unfortunately, clinical observation is often sub-jective, needs significant rater training, does not capture data from participants in their natural environment, and is not scalable for use in large populations or for longitu-dinal monitoring. To address these challenges, we devel-oped and tested a self-contained app designed to measure toddlers' social communication behaviors in a primary care, school, or home setting. Twenty 16-30 month old children with and without autism participated in this study. Tod-dlers watched the developmentally-Appropriate visual stim-uli on an iPad in a pediatric clinic and in our lab while the iPad camera simultaneously recorded video of the child's behaviors. Automated computer vision algorithms coded emotions and social referencing to quantify autism risk be-haviors. We validated our automatic computer coding by comparing the computer-generated analysis of facial expres-sion and social referencing to human coding of these behav-iors. We report our method and propose the development and testing of measures of young children's behaviors as the first step toward development of a novel, fully integrated, low-cost, scalable screening tool for autism and other neu-rodevelopmental disorders of early childhood.
Hashemi, J; Campbell, K; Carpenter, KLH; Harris, A; Qiu, Q; Tepper, M; Espinosa, S; Borg, JS; Marsan, S; Calderbank, R; Baker, JP; Egger, HL; Dawson, G; Sapiro, G
Mobihealth 2015 5th Eai International Conference on Wireless Mobile Communication and Healthcare Transforming Healthcare Through Innovations in Mobile and Wireless Technologies
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