Fuzzy Sets for Human Fall Pattern Recognition

  • Marina V. Sokolova
  • Antonio Fernández-Caballero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7329)


Vision-based fall detection is a challenging problem in pattern recognition. This paper introduces an approach to detect a fall as well as its type in infrared video sequences. The regions of interest of the segmented humans are examined image by image though calculating geometrical and kinematic features. The human fall pattern recognition system identifies true and false falls. The fall indicators used as well as their fuzzy model are explained in detail. The fuzzy model has been tested for a wide number of static and dynamic falls.


  1. 1.
    Benocci, M., Tacconi, C., Farella, E., Benini, L., Chiari, L., Vanzago, L.: Accelerometer-based fall detection using optimized ZigBee data streaming. Microelectronics Journal 41(11), 703–710 (2010)CrossRefGoogle Scholar
  2. 2.
    Doukas, C., Maglogiannis, I.: Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components. IEEE Transactions on Information Technology in Biomedicine 15(2), 277–289 (2011)CrossRefGoogle Scholar
  3. 3.
    Fernández-Caballero, A., Castillo, J.C., Rodríguez-Sánchez, J.M.: Human activity monitoring by local and global finite state machines. Expert Systems with Applications 39(8), 6982–6993 (2012)CrossRefGoogle Scholar
  4. 4.
    Fernández-Caballero, A., Castillo, J.C., Serrano-Cuerda, J., Maldonado-Bascón, S.: Real-time human segmentation in infrared videos. Expert Systems with Applications 38(3), 2577–2584 (2011)CrossRefGoogle Scholar
  5. 5.
    Fernández-Caballero, A., Castillo, J.C., Martínez-Cantos, J., Martínez-Tomás, R.: Optical flow or image subtraction in human detection from infrared camera on mobile robot. Robotics and Autonomous Systems 58(12), 1273–1283 (2010)CrossRefGoogle Scholar
  6. 6.
    Khawandi, S., Daya, B., Chauvet, P.: Implementation of a monitoring system for fall detection in elderly healthcare. Procedia Computer Science 3, 216–220 (2011)CrossRefGoogle Scholar
  7. 7.
    Klack, L., Möllering, C., Ziefle, M., Schmitz-Rode, T.: Future care floor: A sensitive floor for movement monitoring and fall detection in home environments. In: Proceedings of MobiHealth 2010, pp. 211–218 (2010)Google Scholar
  8. 8.
    Litvak, D., Zigel, Y., Gannot, I.: Fall detection of elderly through floor vibrations and sound. In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4632–4635 (2008)Google Scholar
  9. 9.
    Loomis, A.: Figure Drawing for All it’s Worth. Viking Adult (1943)Google Scholar
  10. 10.
    Mo, H.C., Leou, J.J., Lin, C.S.: Human behavior analysis using multiple 2d features and multi category support vector machine. In: Proceedings of the IAPR Conference on Machine Vision Applications, pp. 46–49 (2009)Google Scholar
  11. 11.
    Moreno-Garcia, J., Rodriguez-Benitez, L., Fernández-Caballero, A., López, M.T.: Video sequence motion tracking by fuzzification techniques. Applied Soft Computing 10(1), 318–331 (2010)CrossRefGoogle Scholar
  12. 12.
    Pellegrini, S., Iocchi, L.: Human posture tracking and classification through stereo vision and 3d model matching. EURASIP Journal on Image and Video Processing, 1–12 (2008)Google Scholar
  13. 13.
    Rimminen, H., Lindström, J., Linnavuo, M., Sepponen, R.: Detection of falls among the elderly by a floor sensor using the electric near field. IEEE Transactions on Information Technology in Biomedicine 14(6), 1475–1476 (2010)CrossRefGoogle Scholar
  14. 14.
    Rojas-Albarracín, G., Carbajal, C.A., Fernández-Caballero, A., López, M.T.: Skeleton simplfication by key points identfication. In: Proceedings of the 2nd Mexican Conference on Pattern Recognition, pp. 30–39 (2010)Google Scholar
  15. 15.
    Thome, N., Miguet, S., Ambellouis, S.: A real-time, multiview fall detection system: A LHMM-based approach. IEEE Transactions on In Circuits and Systems for Video Technology 18(11), 1522–1532 (2008)CrossRefGoogle Scholar
  16. 16.
    Wei, X., Chai, J.: Modeling 3d human poses from uncalibrated monocular images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1873–1880 (2009)Google Scholar
  17. 17.
    Yu, X.: Approaches and principles of fall detection for elderly and patient. In: Proceedings of the 10th IEEE HealthCom 2008, pp. 42–47 (2008)Google Scholar
  18. 18.
    Zweng, A., Zambanini, S., Kampel, M.: Introducing a Statistical Behavior Model into Camera-Based Fall Detection. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammoud, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6453, pp. 163–172. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marina V. Sokolova
    • 1
    • 2
  • Antonio Fernández-Caballero
    • 1
    • 3
  1. 1.Instituto de Investigación en Informática de Albacete (I3A)Universidad de Castilla-La ManchaAlbaceteSpain
  2. 2.South-West State UniversityKurskRussia
  3. 3.Departamento de Sistemas InformáticosUniversidad de Castilla-La ManchaAlbaceteSpain

Personalised recommendations