Advertisement

Fall Detection Using Body Volume Recontruction and Vertical Repartition Analysis

  • Edouard Auvinet
  • Franck Multon
  • Alain St-Arnaud
  • Jacqueline Rousseau
  • Jean Meunier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

With the life expectancy increase, more and more elderly people risk to fall at home. In order to help them living safely at home by reducing the eventuality of unrescued fall, autonomous systems are developped. In this paper, we propose a new method to detect falls at home, based on a multiple cameras network for reconstructing the 3D shape of people. Fall events are detected by analyzing the volume distribution along the vertical axis, and an alarm is triggered when the major part of this distribution is abnormaly near the floor which implies that a person has fallen on the floor. This method is evaluated regarding the number of cameras (from 3 to 8) with 22 fall scenarios. Results show 96% of correct detections with 3 cameras and above 99% with 4 cameras and more.

Keywords

Correct Detection Body Volume Fall Detection Fall Event Radial Distortion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Auvinet, E., Reveret, L., St-Arnaud, A., Rousseau, J., Meunier, J.: Fall detection using multiple cameras. In: EMBS 2008, 30th Annual Internationnal Conference of the IEEE (2008)Google Scholar
  2. 2.
    Xinguo, Y.: Approches and Principles of Fall Detection for Elderly and Patient. In: 10th IEEE Intl. Conf on e-Health Networking, Application and Service (2008)Google Scholar
  3. 3.
    Noury, N., Fleury, A., Rumeau, P., Bourke, A., Laighin, G., Rialle, V., Lundy, J.: Fall detection - principles and methods. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), pp. 1663–1666 (2007)Google Scholar
  4. 4.
    Kangas, M., Konttila, A., Lindgren, P., Winblad, I., Jämsä, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait & Posture 28, 285–291 (2008)CrossRefGoogle Scholar
  5. 5.
    Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Monocular 3D Head Tracking to Detect Falls of Elderly People. In: Conference of the IEEE EMBS, August 30-September 3 (2006)Google Scholar
  6. 6.
    Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Fall Detection from human Shape and Motion History using Video Surveillance. In: IEEE First International Workshop on Smart Homes for Tele-Health, Niagara Falls, May 2007, pp. 875–880 (2007)Google Scholar
  7. 7.
    Tao, J., Turjo, M., Wong, M.F., Wang, M., Tan, Y.-P.: Fall Incidents Detection for Intelligent Video Surveillance. In: Conference on Information, Communications and Signal Processing, December 6-9 (2005)Google Scholar
  8. 8.
    Lee, T., Mihailidis, A.: An intelligent emergency response system: preliminary development and testing of automated fall detection. Journal of telemedicine and telecare 11(4), 194–198 (2005)CrossRefGoogle Scholar
  9. 9.
    Miaou, S.G., Shung, P.H., Huang, C.Y.: A customized human fall detection system using omni-camera images and personnal information. In: D2H2 2006, 1st Transdisciplinary Conf. on Distributed Diagnosis and Home Care, pp. 39–42 (2006)Google Scholar
  10. 10.
    Anderson, D., Keller, J.M., Skubic, M., Chen, X., He, Z.: Recognizing Falls from Silhouettes. In: Conference of the IEEE EMBS, August 30-September 3 (2006)Google Scholar
  11. 11.
    Laurentini, A.: The visual Hull concept for silhouette-based image undestanding. IEEE Trans. Pattern Annal. Mach. Intell. 16(2), 150–162 (1994)CrossRefGoogle Scholar
  12. 12.
    Kim, H., Sakamoto, R., Kitahara, I., Orman, N., Toriyama, T., Kogure, K.: Compensated Visual Hull for Defective Segmentation and Occlusion. In: International Conference on Artificial Reality and Telexistence (2007)Google Scholar
  13. 13.
    Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics (2004)Google Scholar
  14. 14.
    Heikkila, Silven: A Four-step Camera Calibration Procedure with Implicit Image Correction. In: Conference Vision Pattern Recognition (1997)Google Scholar
  15. 15.
    Bouguet, J.Y.: Camera Calibration Toolbox for Matlab (2007), http://www.vision.caltech.edu/bouguetj/calib_doc/

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Edouard Auvinet
    • 1
    • 2
  • Franck Multon
    • 2
  • Alain St-Arnaud
    • 3
  • Jacqueline Rousseau
    • 4
  • Jean Meunier
    • 1
  1. 1.IGBUniversity of MontrealMontrealCanada
  2. 2.M2SUniversity of Rennes 2RennesFrance
  3. 3.Health and social service center Lucille-TeasdaleMontrealCanada
  4. 4.CRIUGMUniversity of MontrealMontrealCanada

Personalised recommendations