Computer vision tools for the non-invasive assessment of autism-related behavioral markers

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated that promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests behavioral markers can be observed late in the first year of life. Many of these studies involved extensive frame-by-frame video observation and analysis of a child’s natural behavior. Although non-intrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are impractical for clinical and large population research purposes. Diagnostic measures for ASD are available for infants but are only accurate when used by specialists experienced in early diagnosis. This work is a first milestone in a long-term multidisciplinary project that aims ∗J. Hashemi, T. V. Spina, and M. Tepper equally contributed to this work. This work was partially done while M. Tepper, T. V. Spina, and G. Sapiro were with the University of Minnesota. Work partially supported by NSF Grants 1039741 & 1028076, CAPES (BEX 1018/11-6) and FAPESP (2011/01434-9) PhD scholarships from Brazil, and the U.S. Department of Defense. 1 ar X iv :1 21 0. 70 14 v2 [ cs .C V ] 8 N ov 2 01 2 J. Hashemi et al./Computer vision tools for assessing ASD behavioral markers 2 at helping clinicians and general practitioners accomplish this early detection/measurement task automatically. We focus on providing computer vision tools to measure and identify ASD behavioral markers based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure three critical AOSI activities that assess visual attention. We augment these AOSI activities with an additional test that analyzes asymmetrical patterns in unsupported gait. The first set of algorithms involves assessing head motion by tracking facial features, while the gait analysis relies on joint foreground segmentation and 2D body pose estimation in video. We show results that provide insightful knowledge to augment the clinician’s behavioral observations obtained from real in-clinic assessments.