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Modèles de Markov cachés récents pour la détection et la reconnaissance d’activités à partir d’un capteur IMU placé sur une jambe

Haoyu Li 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : The thesis context is that of the quantified self, a movement born in California that consists in getting to know oneself better by measuring data relating to one’s body and activities. The research work consisted in developing algorithms for analyzing signals from an IMU (Inertial Measurement Unit) sensor placed on the leg to recognize different movement activities such as walking, running, stair climbing... These activities are recognizable by the shape of the sensor’s acceleration and angular velocity signals, both tri-axial, during leg movement and gait cycle.To address the recognition problem, the thesis work resulted in the construction of a particular hidden Markov chain, called semi-triplet Markov chain, which combines a semi-Markov model and a Gaussian mixture model in a triplet Markov model. This model is both adapted to the nature of the gait cycle, and to the sequence of activities as it can be carried out in daily life. To adapt the model parameters to the differences in human morphology and behavior, we have developed algorithms for estimating parameters both off-line and on-line.To establish the classification and learning performance of the algorithms, we conducted experiments on the basis of recordings collected during the thesis and on public dataset. The results are systematically compared with state-of-the-art algorithms.
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Haoyu Li. Modèles de Markov cachés récents pour la détection et la reconnaissance d’activités à partir d’un capteur IMU placé sur une jambe. Autre. Université de Lyon, 2019. Français. ⟨NNT : 2019LYSEC041⟩. ⟨tel-02537351⟩

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