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Toward identifying behavioral risk markers for mental health disorders: an assistive system for monitoring children’s movements in a preschool classroom

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Abstract

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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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA

    Nicholas Walczak, Joshua Fasching, Vassilios Morellas & Nikolaos Papanikolopoulos

  2. Department of Psychiatry, University of Minnesota, Minneapolis, USA

    Kathryn Cullen

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  1. Nicholas Walczak
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  2. Joshua Fasching
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  3. Kathryn Cullen
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  4. Vassilios Morellas
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  5. Nikolaos Papanikolopoulos
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Correspondence to Nikolaos Papanikolopoulos.

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Walczak, N., Fasching, J., Cullen, K. et al. Toward identifying behavioral risk markers for mental health disorders: an assistive system for monitoring children’s movements in a preschool classroom. Machine Vision and Applications 29, 703–717 (2018). https://doi.org/10.1007/s00138-018-0926-y

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