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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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References

  1. Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-euclidean metrics for fast and simple calculus on diffusion tensors. Magn. Reson. Med. 56(2), 411–421 (2006)

    Article  Google Scholar 

  2. Barkley, R.A.: The ecological validity of laboratory and analogue assessment methods of adhd symptoms. J. Abnorm. Child Psychol. 19(2), 149–178 (1991)

    Article  Google Scholar 

  3. Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House, Norwood (1999)

    MATH  Google Scholar 

  4. Bowlby, J.: A Secure Base: Clinical Applications of Attachment Theory. Taylor & Francis, New York (2005)

    Google Scholar 

  5. Chang, M.C., Krahnstoever, N., Ge, W.: Probabilistic group-level motion analysis and scenario recognition. In: IEEE International Conference on Computer Vision, 2011, pp. 747–754 (2011)

  6. Cherian, A., Sra, S., Banerjee, A., Papanikolopoulos, N.: Jensen-Bregman LogDet divergence with application to efficient similarity search for covariance matrices. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2161–2174 (2013)

    Article  Google Scholar 

  7. Elicker, J., Ruprecht, K.M., Anderson, T.: Observing infants and toddlers relationships and interactions in group care. In: Lived Spaces of Infant-Toddler Education and Care, pp. 131–145. Springer (2014)

  8. Esposito, G., Venuti, P., Apicella, F., Muratori, F.: Analysis of unsupported gait in toddlers with autism. Brain Dev 33(5), 367–373 (2011)

    Article  Google Scholar 

  9. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, vol. 96, pp. 226–231. AAAI (1996)

  10. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

  11. Fasching, J., Walczak, N., Morellas, V., Papanikolopoulos, N.: Classification of motor stereotypies in video. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4894–4900. IEEE (2015)

  12. Fasching, J., Walczak, N., Sivalingam, R., Cullen, K., Murphy, B., Sapiro, G., Morellas, V., Papanikolopoulos, N.: Detecting risk-markers in children in a preschool classroom. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1010–1016. IEEE (2012)

  13. Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Int. J. Comput. Vis. (IJCV) 59(2), 167–181 (2004)

    Article  Google Scholar 

  14. Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 267–282 (2008)

    Article  Google Scholar 

  15. Focken, D., Stiefelhagen, R.: Towards vision-based 3-D people tracking in a smart room. In: IEEE International Conference on Multimodal Interfaces, pp. 400–405. IEEE (2002)

  16. Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer Series in Statistics, vol. 1. Springer, Berlin (2001)

    MATH  Google Scholar 

  17. Garvey, C.: Play. Harvard University Press, Cambridge (1990)

    Google Scholar 

  18. Hashemi, J., Tepper, M., Spina, T.V., Esler, A., Morellas, V., Papanikolopoulos, N., Egger, H., Dawson, G., Sapiro, G.: Computer vision tools for low-cost and noninvasive measurement of autism-related behaviors in infants. Autism Research and Treatment (2014)

  19. Keni, B., Rainer, S.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process (2008)

  20. Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M., Shafer, S.: Multi-camera multi-person tracking for easyliving. In: IEEE International Workshop on Visual Surveillance, pp. 3–10. IEEE (2000)

  21. Kuczmarski, R.J., Ogden, C.L., Guo, S.S., Grummer-Strawn, L.M., Flegal, K.M., Mei, Z., Wei, R., Curtin, L.R., Roche, A.F., Johnson, C.L.: 2000 CDC growth charts for the united states: methods and development. Vital and Health Statistics. Series 11, Data from the National Health Survey pp. 1–190 (2002)

  22. Laptev, I.: On space-time interest points. Int. J. Comput. Vis. (IJCV) 64(2–3), 107–123 (2005)

    Article  Google Scholar 

  23. Legendre, A., Munchenbach, D.: Two-to-three-year-old children’s interactions with peers in child-care centres: effects of spatial distance to caregivers. Infant Behav. Dev. 34, 111–125 (2011)

    Article  Google Scholar 

  24. Mittal, A., Davis, L.S.: M\(_2\)Tracker: a multi-view approach to segmenting and tracking people in a cluttered scene. Int. J. Comput. Vis. (IJCV) 51(3), 189–203 (2003)

    Article  Google Scholar 

  25. Munaro, M., Menegatti, E.: Fast RGB-D people tracking for service robots. Auton. Robots 37(3), 227–242 (2014)

    Article  Google Scholar 

  26. Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32–38 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  27. Papon, J., Abramov, A., Schoeler, M., Worgotter, F.: Voxel cloud connectivity segmentation-supervoxels for point clouds. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2027–2034. IEEE (2013)

  28. Porikli, F., Tuzel, O., Meer, P.: Covariance tracking using model update based on lie algebra. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 728–735 (2006)

  29. Rajagopalan, S.S., Dhall, A., Goecke, R.: Self-stimulatory behaviours in the wild for autism diagnosis. In: IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 755–761. IEEE (2013)

  30. Rehg, J.M., Abowd, G.D., Rozga, A., Romero, M., Clements, M.A., Sclaroff, S., Essa, I., Ousley, O.Y., Li, Y., Kim, C., et al.: Decoding children’s social behavior. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3414–3421. IEEE (2013)

  31. Rother, C., Kolmogorov, V., Blake, A.: GrabCut: Interactive foreground extraction using iterated graph cuts. In: ACM Transactions on Graphics (TOG), vol. 23, pp. 309–314. ACM (2004)

  32. Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation, pp. 3212–3217. IEEE (2009)

  33. Torrens, P.M., Griffin, W.A.: Exploring the micro-social geography of childrens interactions in preschool: a long-term observational study and analysis using geographic information technologies. Environ. Behav. 45(5), 584–614 (2013)

    Article  Google Scholar 

  34. Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Lecture Notes in Computer Science, vol. 3952, pp. 589–600. Springer (2006)

  35. Walczak, N., Fasching, J., Toczyski, W.D., Morellas, V., Sapiro, G., Papanikolopoulos, N.: Locating occupants in preschool classrooms using a multiple RGB-D sensor system. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2166–2172. IEEE (2013)

  36. World Health Organization: The World Health Report 2004: Changing History, Annex Table 3: Burden of Disease in DALYs by Cause, Sex, and Mortality Stratum in WHO Regions, Estimates for 2002. WHO, Geneva (2004)

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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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  • DOI: https://doi.org/10.1007/s00138-018-0926-y

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