This article is part of the series Signal Processing Technologies for Ambient Intelligence in Home-Care Applications.

Open Access Research Article

The PARAChute Project: Remote Monitoring of Posture and Gait for Fall Prevention

David J Hewson1*, Jacques Duchêne1, François Charpillet2, Jamal Saboune2, Valérie Michel-Pellegrino1, Hassan Amoud1, Michel Doussot1, Jean Paysant3, Anne Boyer2 and Jean-Yves Hogrel4

Author Affiliations

1 Institute Charles Delaunay, FRE CNRS 2848, University of Technology of Troyes, Troyes 10000, France

2 UMR LORIA 7503, Université de Nancy, CNRS-INRIA, Campus Scientifique, BP 239, Vandoeuvre-lès-Nancy 54506, France

3 Institut régional de Réadaptation, Faculté de medicine, 9 Avenue de la Forêt de Haye, BP 184, Vandoeuvre 54500, France

4 Neuromuscular Physiology Laboratory, Institut of Myology, GH Pitié-Salpêtrière, Paris 75651, France

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EURASIP Journal on Advances in Signal Processing 2007, 2007:027421 doi:10.1155/2007/27421


The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2007/1/027421


Received:10 March 2006
Revisions received:19 October 2006
Accepted:21 February 2007
Published:23 April 2007

© 2007 Hewson et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Falls in the elderly are a major public health problem due to both their frequency and their medical and social consequences. In France alone, more than two million people aged over 65 years old fall each year, leading to more than 9 000 deaths, in particular in those over 75 years old (more than 8 000 deaths). This paper describes the PARAChute project, which aims to develop a methodology that will enable the detection of an increased risk of falling in community-dwelling elderly. The methods used for a remote noninvasive assessment for static and dynamic balance assessments and gait analysis are described. The final result of the project has been the development of an algorithm for movement detection during gait and a balance signature extracted from a force plate. A multicentre longitudinal evaluation of balance has commenced in order to validate the methodologies and technologies developed in the project.

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