Mental health disorders are a leading cause of disability in North America. An important aspect in treating mental disorders is early intervention, which dramatically increases the probability of positive outcomes; however, early intervention hinges upon knowledge and detection of risk markers for particular disorders. Ideally, the screening of these risk markers should occur in a community setting, but this is time-consuming and resource-intensive. Assistive systems could greatly aid in the detection of risk markers in a hectic environment like a preschool classroom. This paper presents a multi-sensor system consisting of 5 RGB-D sensors that detects and tracks the location of occupants in a preschool classroom and computes a measure of activity level and proximity between individuals, an index of social functioning. This assistive system operates in near real-time and is able to track occupants and deal with difficult situations both with occupants (children sitting and laying on the ground, hugging, playing dress-up, etc) and their environment (i.e., changing light levels from artificial and natural sources). The system is installed at, and validated on recordings taken from, the Shirley G. Moore Lab School, a research preschool classroom at the University of Minnesota. The work described herein provides the initial groundwork for monitoring basic elements of child behavior; future efforts will be geared toward identifying and tracking more sophisticated behavioral signatures relevant to mental health.
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Acknowledgements This material is based upon work supported by the National Science Foundation through Grants IIP-0443945, CNS-0821474, IIP-0934327, CNS-1039741, SMA-1028076, and CNS-1338042.
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