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)


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.


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.


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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

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