Real-time fall detection method based on Hidden Markov modelling

Abstract

In the next few decades the increase of the number of elderly people will be of major concern, so that solutions must be found in order to maintain them at home. However such a population is exposed to the risk of falls, that can lead to dependency. This paper recalls some approaches used for fall detection and focuses on a method based on an uncalibrated camera. Motion detection uses a combination of simple Gaussian background modelling and interframe difference for person shape detection and features extraction. These features feed a Hidden Markov Model dedicated to fall detection. The algorithm has been tested on real data and we show that simple techniques can be used in order to obtain a fast and reliable fall detection system. © 2012 Springer-Verlag.