2026-09-21T22:10:00Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/97347 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Forsyth, David Forsyth, David Hoiem, Derek Lazebnik, Lana Karahalios, Karrie Rehg, James Tsatsoulis, Penelope Daphne 2017-08-10T19:14:58Z 2017-08-10T19:14:58Z 2017-04-19 2017-05 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. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms The student, Penelope Tsatsoulis, accepted the attached license on 2017-04-13 at 10:13. The student, Penelope Tsatsoulis, submitted this Dissertation for approval on 2017-04-13 at 10:21. This Dissertation was approved for publication on 2017-04-19 at 09:58. DSpace SAF Submission Ingestion Package generated from Vireo submission #10737 on 2017-08-10 at 13:39:37 Made available in DSpace on 2017-08-10T19:14:58Z (GMT). No. of bitstreams: 2 TSATSOULIS-DISSERTATION-2017.pdf: 74241529 bytes, checksum: 0bc0b908522c82b58b6af27ff034c5b6 (MD5) LICENSE.txt: 4216 bytes, checksum: cc9c2c08438699f4178b83893489854a (MD5) Previous issue date: 2017-04-19 application/pdf http://hdl.handle.net/2142/97347 en Copyright 2017 Penelope Tsatsoulis Artificial intelligence Computer vision Attention Recognition Engagement Autism Spectrum Disorders Automatically predicting child engagement in dyadic interactions text text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.