Motion Analysis By Video For Gait Evaluation With Innovative Technology
Mehran Hatamzadeh
Doctoral Candidate in 2nd year
Abstract
In France, the expenses in physical rehabilitation increased from 7.3 to 8.4 B€ between 2010 and 2015, mainly due to the ageing population, the increase of chronic pathologies such as strokes or Parkinson, and the shortening of the hospitalization time. 70% of the activity of rehabilitation institutions is about gait (first step for the regain of autonomy). Accurate reliable knowledge of gait characteristics at a given time, and even more importantly, monitoring and evaluating them over time, may enable early diagnosis of diseases and their complications and help to find the best treatment. Three-dimensional motion analysis is the gold standard for clinical gait analysis (CGA), particularly in the presence of pathologies that hamper walking. Today, less than 1% of the patients benefit from CGA.
The main objective of this project is to develop a method based on an innovative low-cost motion analysis system and machine learning, enabling an accurate quantification of gait deviation parameters during functional tests, including spatiotemporal and full-body kinematic parameters.
The main objective of this project is to develop a method based on an innovative low-cost motion analysis system and machine learning, enabling an accurate quantification of gait deviation parameters during functional tests, including spatiotemporal and full-body kinematic parameters.
Supervisors
- Raphael Zory, Full Professor, Director of LAMHESS, Université Côte d'Azur
- Laurent Buse, Senior Research Scientist, Aromath Team, INRIA
- Tutor from Academia
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Olivier Mestre, i3S Laboratory, Université Côte d'Azur
- Mentor from Industry
- Sylvain Benito, ExactCure Company
International 6-months secondment in Canada
in the supervision of Katia Turcot, Assistant Professor, Department of Kinesiology, Laval University, Quebec