Design of a Bullying Detection/Alert System for School-Wide Intervention

  • Sheryl Brahnam
  • Jenifer J. Roberts
  • Loris Nanni
  • Cathy L. Starr
  • Sandra L. Bailey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9170)

Abstract

In this paper we propose a bullying detection/alert system for school-wide intervention that combines wearables with heart rate (HR) monitors, surveillance cameras, multimodal machine learning, cloud computing, and mobile devices. The system alerts school personnel when potential bullying is detected and identifies potential bullying in three ways: (i) by tracking and assessing the proximity of known bullies to known students at risk for bullying; (ii) by monitoring stress levels of students via HR analysis; and (iii) by recognizing actions, emotions, and crowd formations associated with bullying. We describe each of these components and their integration, noting that it is possible for the system to use only a network of surveillance cameras. Alerts produced by the system can be logged. Reviews of these logs and tagged videos of detected bullying would allow school personnel to review incidents and their methods for handling bullying by providing more information about the locations, causes, and actors involved in bullying as well as teacher/staff response rates. In addition, false positives could be marked and fed back to the system for relearning and continuous improvement of the system.

Keywords

School bullying Machine learning Heart-rate monitoring Face tracking Emotion classification Action classification Computer technology 

References

  1. 1.
    Arandjelovic, O.: Crowd detection from still images. In: British Machine Vision Conference (BMVC). University of Leeds (2008)Google Scholar
  2. 2.
    Arseneault, L., Walsh, E., Trzesniewski, K., Newcombe, R., Caspi, A., Moffitt, T.E.: Bullying victimization uniquely contributes to adjustment problems in young children: a nationally representative cohort study. Pediatrics 118, 130–138 (2006)CrossRefGoogle Scholar
  3. 3.
    Atlas, R.S., Pepler, D.: Observations of bullying in the classroom. J. Educ. Res. 92(2), 86–99 (1998)CrossRefGoogle Scholar
  4. 4.
    Barhight, L.R., Hubbard, J.A., Hyde, C.T.: Children’s physiological and emotional reactions to witnessing bullying predict bystander intervention. Child Dev. 84(1), 375–390 (2013)CrossRefGoogle Scholar
  5. 5.
    Bettadapura, V.: Face Expression Recognition and Analysis: The State of the Art, Cornell University Library, arXiv.org (2012)Google Scholar
  6. 6.
    Brahnam, S.B., Nanni, L.: High performance set of features for human action classification. In: International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), Las Vegas, NV, pp. 980–984 (2009)Google Scholar
  7. 7.
    Farrington, D.P., Ttofi, M.M.: School-based programs to reduce bullying and victimization: a systematic review for the campbell collaboration crime and justice group. U.S. Department of Justice (2010). http://www.ncjrs.gov/pdffiles1/nij/grants/229377.pdf
  8. 8.
    Fekkes, M., Pijpers, F.I.M., Fredriks, A.M., Vogels, T., Verloove-Vanhorick, P.: Do bullied children get ill, or do ill children get bullied? A prospective cohort study on the relationship between bullying and health-related symptoms. Pediatrics 117, 1568–1574 (2006)CrossRefGoogle Scholar
  9. 9.
    Hanish, L.D., Guerra, N.G.: A longitudinal analysis of patterns of adjustment following peer victimization. Dev. Psychopathol. 14, 69–89 (2002)CrossRefGoogle Scholar
  10. 10.
    Hassner, T., Itcher, Y., Kliper-Gross, O.: Violent flows: real-time detection of violent crowd behavior. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–6 (2012)Google Scholar
  11. 11.
    Hatzenbuehler, M.L., Keyes, K.M.: Inclusive anti-bullying policies and reduced risk of suicide attempts in lesbian and gay youth. J. Adolesc. Health 53(suppl. 1), S21–S26 (2013)CrossRefGoogle Scholar
  12. 12.
    Hertz, M.F., Donato, I., Wright, J.: Bullying and suicide: a public health approach. J. Adolesc. Health 53, S1–S3 (2013)CrossRefGoogle Scholar
  13. 13.
    Hoque, M.E., Picard, R.: Acted vs. natural frustration and delight: many people smile in natural frustration. In: 9th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2011), Santa Barbara, CA (2011)Google Scholar
  14. 14.
    Ishii, I., Ichida, T., Gu, Q., Takaki, T.: 500-Fps face tracking system. J. Real-Time Image Process. 8(4), 379–388 (2013)CrossRefGoogle Scholar
  15. 15.
    Juvonen, J., Graham, S.: Bullying in schools: the power of bullies and the plight of victims. Annu. Rev. Psychol. 65, 159–185 (2014)CrossRefGoogle Scholar
  16. 16.
    Juvonen, J., Wang, Y., Espinoza, G.: Bullying experiences and compromised academic performance across middle school grades. J. Early Adolesc. 31, 152–173 (2011)CrossRefGoogle Scholar
  17. 17.
    Kaltiala-Heino, R., Rimpela, M., Mamunen, M., Rimpela, A., Rantanen, P.: Bullying, depression, and suicidal ideation in finnish adolescents: school survey. Br. Med. J. 319, 348–351 (1999)CrossRefGoogle Scholar
  18. 18.
    Knack, J.M., Jensen-Campbell, L.A., Baum, A.: Worse than sticks and stones? bullying is associated with altered HPA axis functioning and poorer health. Brain Cogn. 77, 183–190 (2011)CrossRefGoogle Scholar
  19. 19.
    Munaro, M.B., Ghidoni, S., Tartaro, D.T., Menegatti, E.: A feature-based approach to people re-identification using skeleton keypoints. In: IEEE International Conference on Robotics and Automation, Hong Kong, China (2014)Google Scholar
  20. 20.
    Nanni, L., Brahnam, S., Ghidoni, S., Menegatti, E.: Automated crowd detection in stadium arenas. Northeast Decision Sciences Institute 2013. New York City, pp. 536–545 (2013)Google Scholar
  21. 21.
    Nanni, L., Munaro, M., Ghidoni, S., Menegatti, E., Brahnam, S.: Ensemble of different approaches for a reliable person re-identification system. Appl. Comput. Inform. (in press)Google Scholar
  22. 22.
    Olweus, D.: What We Know About Bullying. Blackwell Publishers Inc., Malden (1993)Google Scholar
  23. 23.
    Pepler, D., Jiang, D., Craig, W., Connolly, J.: Development trajectories of bullying and associated factors. Child Dev. 79(2), 325–338 (2008)CrossRefGoogle Scholar
  24. 24.
    Poh, M.-Z., Mcduff, D.J., Picard, R.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18(10), 10762–10774 (2010)CrossRefGoogle Scholar
  25. 25.
    Robers, S., Zhang, J., Truman, J.: Indicators of School Crime and Safety: 2011, National Center for Education Statistics, U.S. Department of Education, and Bureau of Justice Statistics, Office of Justice Programs, U.S. Department of Justice, Washington, DC (2012)Google Scholar
  26. 26.
    Tattum, D.: Violence and Aggression in Schools, Trentham Books, Stoke-on-Trent, pp. 7–20 (1989)Google Scholar
  27. 27.
    U.S. Department of Justice Bureau of Justice Statistics. School Crime Supplement (Scs) to the National Crime Victimization Survey (2011)Google Scholar
  28. 28.
    Vezzani, R., Baltieri, D., Cucchiara, R.: People re-identification in surveillance and forensics: a survey. ACM Comput. Surv. 46(2), 29:1–29:3 (2013)CrossRefGoogle Scholar
  29. 29.
    Vossekuil, B., Fein, R.A., Reddy, M., Borum, R., Modzeleski, W.: The final report and findings of the safe school initiative: implications for the prevention of school attacks in the United States, Washington, DC (2002)Google Scholar
  30. 30.
    Wang, B., Ye, M., Li, X., Zhao, F., Ding, J.: Abnormal crowd behavior detection using high-frequency and spatio-temporal features. Mach. Vis. Appl. 23(3), 501–511 (2012)CrossRefGoogle Scholar
  31. 31.
    Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. (TOG) 31(4) (2012)Google Scholar
  32. 32.
    Wurf, G.: High school anti-bullying interventions: an evaluation of curriculum approaches and the method of shared concern in four Hong Kong international schools. Aust. J. Guidance Counselling 22(1), 139–149 (2012)CrossRefGoogle Scholar
  33. 33.
    Ye, L., Ferdinando, H., Seppänen, T., Seppänen, E.: Physical violence detection for preventing school bullying. Adv. Artif. Intell. 2014, ID 740358, 1–9 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sheryl Brahnam
    • 1
  • Jenifer J. Roberts
    • 2
  • Loris Nanni
    • 3
  • Cathy L. Starr
    • 2
  • Sandra L. Bailey
    • 2
  1. 1.Computer Information SystemsMissouri State UniversitySpringfieldUSA
  2. 2.Fashion and Interior DesignMissouri State UniversitySpringfieldUSA
  3. 3.DEIUniversity of PaduaPaduaItaly

Personalised recommendations