Analysis of three-dimensional information from multi-camera systems for the detection of the fall and gait analysis

This thesis is concerned with defining new clinical investigation method to assess the impact of ageing on motricity. In particular, this thesis focuses on two main possible disturbance during ageing : the fall and walk impairment. This two motricity disturbances still remain unclear and their clinical analysis presents real scientist and technological challenges. In this thesis, we propose novel measuring methods usable in everyday life or in the walking clinic, with a minimum of technical constraints.In the first part, we address the problem of fall detection at home, which was widely discussed in previous years. In particular, we propose an approach to exploit the subject’s volume, reconstructed from multiple calibrated cameras. These methods are generally very sensitive to occlusions that inevitably occur in the home and we therefore propose an original approach much more robust to these occultations. The efficiency and realtime operation has been validated on more than two dozen videos of falls and lures, with results approaching 100 % sensitivity and specificity with at least four or more cameras.In the second part, we go a little further in the exploitation of reconstructed volumes of a person at a particular motor task : the treadmill, in a clinical diagnostic. In this section we analyze more specifically the quality of walking. For this we develop the concept of using depth camera for the quantification of the spatial and temporal asymmetry of lower limb movement during walking. After detecting each step in time, this method makes a comparison of surfaces of each leg with its corresponding symmetric leg in the opposite step. The validation performed on a cohort of 20 subjects showed the viability of the approach.

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Source https://theses.hal.science/tel-00946188
Author Auvinet, Edouard
Maintainer CCSD
Last Updated May 6, 2026, 13:25 (UTC)
Created May 6, 2026, 13:25 (UTC)
Identifier NNT: 2012REN20067
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Laboratoire de Physiologie et de Biomécanique de l'Exercice Musculaire. UHB ; Université de Rennes 2 (UR2)
creator Auvinet, Edouard
date 2012-06-14T00:00:00
harvest_object_id 2bb3d6f8-5a15-4063-afab-1d02c5698f7c
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-03-31T00:00:00
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