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Autism Res Treat. 2014;2014:935686. doi: 10.1155/2014/935686. Epub 2014 Jun 22.

Computer vision tools for low-cost and noninvasive measurement of autism-related behaviors in infants.

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  • 1Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.
  • 2Institute of Computing, University of Campinas, 13083 Campinas, SP, Brazil.
  • 3Department of Pediatrics, University of Minnesota, Minneapolis, MN 55455, USA.
  • 4Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
  • 5Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 22708, USA.
  • 6Department of Psychiatry and Behavioral Sciences and School of Medicine, Duke University, Durham, NC 27708, USA.
  • 7Department of Electrical and Computer Engineering, Department of Computer Science, and Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.


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.

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