TY - GEN
T1 - A computer vision approach for the assessment of autism-related behavioral markers
AU - Hashemi, Jordan
AU - Spina, Thiago Vallin
AU - Tepper, Mariano
AU - Esler, Amy N
AU - Morellas, Vassilios
AU - Papanikolopoulos, Nikolaos P
AU - Sapiro, Guillermo
PY - 2012
Y1 - 2012
N2 - 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 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 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 facial feature tracking, 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.
AB - 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 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 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 facial feature tracking, 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.
UR - http://www.scopus.com/inward/record.url?scp=84872844561&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872844561&partnerID=8YFLogxK
U2 - 10.1109/DevLrn.2012.6400865
DO - 10.1109/DevLrn.2012.6400865
M3 - Conference contribution
AN - SCOPUS:84872844561
SN - 9781467349635
T3 - 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL 2012
BT - 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL 2012
T2 - 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL 2012
Y2 - 7 November 2012 through 9 November 2012
ER -