CDI-Type II: Computational Tools for Behavioral Analysis, Diagnosis, and Intervention of at Risk Children

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This project will develop algorithms to assist with the early diagnosis of children who are at risk of developing behavioral disorders. Previous research has indicated that two critical areas of behavioral investigation for use in identifying at-risk children have been abnormalities in motor activities and emotional range displays, especially of the face. Motor abnormalities are based on the observation that motor control involves the circuits of the brain associated with dopamine; these are also implicated in behavioral disorders. Many different disorders share the observation of disruption in the emotional range regulation, so facial expressions are included in the study.To date, assessments of motor and emotional range have been done by the experts who view and rate videos of an individual. However, these expert, subjective ratings limit the analysis of behavioral conditions to only a narrow range of behaviors, work only for small populations of individual subjects, and are both costly and dependent on the observer's particular expertise. In order to enable wider population screening, automation is required. Innovative ways of capturing and quantifying the expertise of experts will be accompanied by metrics for assessing the evolution of the behavior. In addition, new computational tools will support evaluation of the effectiveness of interventions.The broader impacts of the proposed work will involve improved mental health levels across the populations by providing a systematic approach for enhancing early detection, prevention, or mitigation of behavioral disorders and likely reduce the long-term costs of missed or late diagnosis. The research results will be blended with the educational process through inclusion of project themes in the curricula at the Institute of Technology, the Medical School, and the College of Education and Human Development at the University of Minnesota and the creation of a program with annual workshops, tutorials, web pages and a wiki on knowledge discovery and behavioral analysis. The team will develop an interactive exhibit for children at the Bakken Museum, and create of new instructional material for student teachers at the Institute of Child Development and similar institutions. Development of a central web repository will insure that the algorithms and the data will be readily available for appropriate research.