A new Framework for Elderly Fall Detection
Using Coupled Hidden Markov Models
Mabrouka Hagui1, Mohamed Mahjoub1,
and Faycel ElAyeb2
1National Engineering School of Sousse, University of
Sousse, Laboratory of Advanced Technology and Intelligent Systems, Tunisia
2Preparatory Institute for Engineering Studies, University
of Monastir, Tunisia
Abstract:
Falls
are a most common problem for old people. They can result in dangerous
consequences even death. Many recent works have presented different approaches
to detect fall and prevent dangerous outcomes. In this paper, human fall
detection from video streams based on a Coupled Hidden Markov Model (CHMM) has
been proposed. The CHMM was used to model the motion and static spatial
characteristic of human silhouette. The validity of current proposed method was
demonstrated with experiments on Le2i database, Weizman database and video from
Youtube simulating falls and normal activities. Experimental results showed the
superiority of the CHMM for video fall detection.
Keywords: Fall detection; feature extraction; shape deformation, motion history of
image, coupled hidden markov models.
Received June 17, 2016; accepted July 28, 2016