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Approche topologique de la métrologie du mouvement pour des applications en réalité virtuelle

Abstract : In the medical field, a better knowledge of the motor function isimportant for us to determine therapies adapted to each motor lesion andtools of studies and screening for neurodegenerative diseases. In thedomain of virtual reality, motion recognition is an issue in theinteraction of the avatar or the user in immersion with theirenvironment.Several studies have been conducted with the aim of proposingapproaches to the classification of human movement. The main idea ofthese methods is to extract invariants from the recorded data in orderto order them into clusters. However, the study of human motion withmotion capture systems generates a big quantity of data with nonlinearrelations between them. The presented methods in the scientificliterature use these data either directly as input to classificationalgorithms or by applying a dimensional reduction method such asprincipal component analysis prior to classification. These methodsremain extremely sensitive to white noise during recording as well asmorphological differences between subjects.In our work, we will present a methodology of classification andrecognition of human movement which is based on the topologicalanalysis of kinematic data. Topological analysis will be performed viahomological persistence which is a large data analysis method thatallows them to be topologically signed. This method of topologicalanalysis will be combined with learning algorithms to increase theaccuracy of motion recognition by reducing the impact of morphologicaldifferences between subjects, as well as the impact of white noiseissued during the step of movement acquisition. Also, we will combinethe topological analysis method with a temporal neural networkalgorithm in order to build an approach that allows to predict thecontinuation of a movement from a part of a recording interval.The results showed the ability of the proposed approach to achievehigh accuracy at classification, as well as its robustness againstwhite noise and morphological differences between subjects. Theresults also showed the high cost in computing time of our approachwhich we tried to reduce by modifying its steps and by rewriting thecode so that it can be executed in parallel.
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Submitted on : Friday, March 1, 2019 - 4:54:09 PM
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  • HAL Id : tel-02054184, version 1


Chakib Bensekka. Approche topologique de la métrologie du mouvement pour des applications en réalité virtuelle. Traitement du signal et de l'image [eess.SP]. Ecole nationale supérieure d'arts et métiers - ENSAM, 2018. Français. ⟨NNT : 2018ENAM0040⟩. ⟨tel-02054184⟩



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