Citation

Auvinet E, Multon F, Saint-Arnaud A, Rousseau J, Meunier J. IEEE Trans. Inf. Technol. Biomed. 2011; 15(2): 290-300.

Copyright

(Copyright © 2011, Institute of Electrical and Electronics Engineers)

DOI

10.1109/TITB.2010.2087385

PMID

20952341

Abstract

According to the demographic evolution in industrialized countries, more and more elderly people will experience falls at home and will require emergency services. The main problem comes from fall-prone elderly living alone at home. To resolve this lack of safety, 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 abnormally near the floor during a predefined period of time, which implies that a person has fallen on the floor. This method was validated with videos of a healthy subject who performed 24 realistic scenarios showing 22 fall events and 24 cofounding events (11 crouching position, 9 sitting position, 4 lying on a sofa position) under several camera configurations and achieved 99.7% sensitivity and specificity or better with 4 cameras or more. A real-time implementation using GPU reached 10 frames per second (fps) with 8 cameras and 16 fps with 3 cameras.

Language: en