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Title:Automatically predicting child engagement in dyadic interactions
Author(s):Tsatsoulis, Penelope Daphne
Director of Research:Forsyth, David
Doctoral Committee Chair(s):Forsyth, David
Doctoral Committee Member(s):Hoiem, Derek; Lazebnik, Lana; Karahalios, Karrie; Rehg, James
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Artificial intelligence
Computer vision
Autism Spectrum Disorders
Abstract:1 in 68 American 8-year-old children are diagnosed with Autism Spectrum Disorders (ASDs). Though prevalent, ASDs are not typically diagnosed until children are older than 4 years. The results of clinical interventions are improved when children are diagnosed as young as possible but it is difficult to provide clinical evaluation and intervention for all children at such a young age. An automatic method that screened and flagged at-risk children could reach more children and would facilitate the speed of diagnosis. Engagement characterizes an individual's attention to and interaction with the people and objects in their environment. It is used by psychologists to measure a child's social development and a lack of engagement can signal delays, such as ASDs. We demonstrate the first methods capable of predicting the engagement of a child automatically. We apply computer vision techniques to predict child engagement during unscripted two-person interactions. We show that predicting engagement is a challenging task for automatic methods and non-expert people. The work in this thesis provides the first steps to creating an automatic screener for developmental delays.
Issue Date:2017-04-19
Rights Information:Copyright 2017 Penelope Tsatsoulis
Date Available in IDEALS:2017-08-10
Date Deposited:2017-05

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