Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
View-invariant Estimation of Height and Stride for Gait Recognition
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Gait-Based Recognition of Humans Using Continuous HMMs
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Support Vector Data Description
Machine Learning
Using Interval Particle Filtering for Marker Less 3D Human Motion Capture
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Gait recognition using linear time normalization
Pattern Recognition
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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 9000 deaths, in particular in those over 75 years old (more than 8000 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.