Advertisement

Towards a Rodent Tracking and Behaviour Detection System in Real Time

  • José Arturo Cocoma-OrtegaEmail author
  • Jose Martinez-Carranza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524)

Abstract

To analyze rodent behaviors in non-conditioned animal models is an important task that enables a researcher to elaborated conclusions about the effects in the behavior after drug application. Because the amount of data generated in the use of this kind of test, an automatized system that can record these behaviors becomes relevant. There are several proposals aiming at identifying and tracking the rodent in the open field maze, however, behavior identification is a highly desirable feature that is not included. Other works can identify behaviors, but due to high computational costs, special computers or devices are required. In this work, we propose an automatic system based on features computed by a stochastic filter that allows the development of rules to detect specific behaviors exhibited in the open field maze. We demonstrate that it is possible to track a rodent and identify behaviors in real-time (30 fps) and also in high speed (>100Hz) without the need of powerful devices or special conditions for the environment.

Keywords

Rodent tracking Behavior detection Extended Kalman Filter 

Notes

Acknowledgments

We thank Ilhuicamina Daniel Limón Pérez de León, Ph.D., head research of Neuroscience laboratory from Benemérita Universidad Autónoma de Puebla for provided to us the data set for the evaluation and for the guidance about the behaviors that are interesting to detect in the open field maze.

References

  1. 1.
    da Silva Aragão, R., Rodrigues, M.A.B., de Barros, K.M.F.T., Silva, S.R.F., Toscano, A.E., de Souza, R.E., Manhães-de-Castro, R.: Automatic system for analysis of locomotor activity in rodents–A reproducibility study. J. Neurosci. Methods 195(2), 216–221 (2011)CrossRefGoogle Scholar
  2. 2.
    Howerton, C.L., Garner, J.P., Mench, J.A.: A system utilizing radio frequency identification (RFID) technology to monitor individual rodent behavior in complex social settings. J. Neurosci. Methods 209(1), 74–78 (2012)CrossRefGoogle Scholar
  3. 3.
    van Dam, E.A., van der Harst, J.E., ter Braak, C.J., Tegelenbosch, R.A., Spruijt, B.M., Noldus, L.P.: An automated system for the recognition of various specific rat behaviours. J. Neurosci. Methods 218(2), 214–224 (2013)CrossRefGoogle Scholar
  4. 4.
    Sourioux, M., et al.: 3-D motion capture for long-term tracking of spontaneous locomotor behaviors and circadian sleep/wake rhythms in mouse. J. Neurosci. Methods 295, 51–57 (2018)CrossRefGoogle Scholar
  5. 5.
    Chanchanachitkul, W., Nanthiyanuragsa, P., Rodamporn, S., Thongsaard, W., Charoenpong, T.: A rat walking behavior classification by body length measurement. In: The 6th 2013 Biomedical Engineering International Conference, pp. 1–5 (2013)Google Scholar
  6. 6.
    Clarke, R.L., Smith, R.F., Justesen, D.R.: An infrared device for detecting locomotor activity. Behav. Res. Methods Instrum. Comput. 17(5), 519–525 (1985)CrossRefGoogle Scholar
  7. 7.
    Gapenne, O., Simon, P., Lannou, J.: A simple method for recording the path of a rat in an open field. Behav. Res. Methods Instrum. Comput. 22(5), 443–448 (1990)CrossRefGoogle Scholar
  8. 8.
    Geuther, B.Q., et al.: Robust mouse tracking in complex environments using neural networks. Commun. Biol. 2(1), 124 (2018)CrossRefGoogle Scholar
  9. 9.
    Giancardo, L., Sona, D., Scheggia, D., Papaleo, F., Murino, V.: Segmentation and tracking of multiple interacting mice by temperature and shape information. In: Proceedings of the 21st International Conference on Pattern Recognition, ICPR 2012, pp. 2520–2523 (2012)Google Scholar
  10. 10.
    Hong, W., et al.: Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning. Proc. Nat. Acad. Sci. 112(38), E5351–E5360 (2015)CrossRefGoogle Scholar
  11. 11.
    Jia, Y., Wang, Z., et al.: A wirelessly-powered homecage with animal behavior analysis and closed-loop power control. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6323–6326 (2016)Google Scholar
  12. 12.
    Lai, P.L., Basso, D.M., Fisher, L.C., Sheets, A.L.: 3d tracking of mouse locomotion using shape-from-silhouette techniques (2011)Google Scholar
  13. 13.
    Linares-Sánchez, L.J., Fernández-Alemán, J.L., García-Mateos, G., Pérez-Ruzafa, Á., Sánchez-Vázquez, F.J.: Follow-me: a new start-and-stop method for visual animal tracking in biology research. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 755–758 (2015)Google Scholar
  14. 14.
    Macrì, S., Mainetti, L., Patrono, L., Pieretti, S., Secco, A., Sergi, I.: A tracking system for laboratory mice to support medical researchers in behavioral analysis. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4946–4949 (2015)Google Scholar
  15. 15.
    Sebov, K.: Deep rearing. Stanford University, Technical report (2017)Google Scholar
  16. 16.
    Seibenhener, M., Wooten, M.C.: Use of the open field maze to measure locomotor and anxiety-like behavior in mice. J. Vis. Exp. (JoVE) 96, e52434 (2015)Google Scholar
  17. 17.
    Shi, Q., Miyagishima, S., Fumino, S., Konno, S., Ishii, H., Takanishi, A.: Development of a cognition system for analyzing rat’s behaviors. In: 2010 IEEE International Conference on Robotics and Biomimetics, pp. 1399–1404 (2010)Google Scholar
  18. 18.
    da Silva Monteiro, J.P.: Automatic Behavior Recognition in Laboratory Animals using Kinect. Master’s thesis, Faculdade de Engenharia da Universidade do Porto (2012)Google Scholar
  19. 19.
    Tungtur, S.K., Nishimune, N., Radel, J., Nishimune, H.: Mouse behavior tracker: an economical method for tracking behavior in home cages. BioTechniques 63(5), 215–220 (2017)CrossRefGoogle Scholar
  20. 20.
    Wang, Z., Mirbozorgi, S.A., Ghovanloo, M.: Towards a kinect-based behavior recognition and analysis system for small animals. In: 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4 (2015)Google Scholar
  21. 21.
    Wilson, J.C., Kesler, M., Pelegrin, S.L.E., Kalvi, L., Gruber, A., Steenland, H.W.: Watching from a distance: A robotically controlled laser and real-time subject tracking software for the study of conditioned predator/prey-like interactions. J. Neurosci. Methods 253, 78–89 (2015)CrossRefGoogle Scholar
  22. 22.
    Xie, X.S., et al.: Rodent Behavioral Assessment in the Home Cage using the Smartcage™ System, pp. 205–222. Humana Press, Totowa, NJ (2012)Google Scholar
  23. 23.
    Ziegelaar, M.: Development of an inexpensive, user modifiable automated video tracking system for rodent behavioural tests. Master’s thesis, School of Mechanical and Mining Engineering (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • José Arturo Cocoma-Ortega
    • 1
    Email author
  • Jose Martinez-Carranza
    • 1
    • 2
  1. 1.Instituto Nacional de Astrofísica, Óptica y ElectrónicaCholulaMexico
  2. 2.University of BristolBristolUK

Personalised recommendations