Modèles statistiques avancés pour la reconnaissance de l’activité physique dans un environnement non contrôlé en utilisant un réseau d’objets connectés

Abstract : With the arrival of connected objects, the recognition of physical activity is experiencing a new era. New considerations need to be taken into account in order to achieve a better treatment process. In this thesis, we explored the treatment process for recognizing physical activity in an uncontrolled environment. The recognized physical activities, with only one inertial unit (accelerometer, gyroscope and magnetometer), are called elementary. Other types of context-dependent activities are called "context-based". We extracted the DCT as the main descriptor for the recognition of elementary activities. In order to recognize the physical activities based on the context, we defined three levels of granularity: a first level depending on embedded connected objects (smartphone, smartwatch and samrt TV . A second level concerns the study of participants' behaviors interacting with the smart TV screen. The third level concerns the study of participants' attention to TV. We took into consideration the imperfection aspect of the data by merging the multi sensor data with the Dempster-Shafer model. As such, we have proposed different approaches for calculating and approximating mass functions. In order to avoid calculating and selecting the different descriptors, we proposed an approach based on the use of deep learning algorithms (DNN).We proposed two models: a first model consisting of recognizing the elementary activities by selecting the DCT as the main descriptor (DNN-DCT). The second model is to learn raw data from context-based activities (CNN-raw). The disadvantage of the DNN-DCT model is that it is fast but less accurate, while the CNN-raw model is more accurate but very slow. We have proposed an empirical study to compare different methods that can accelerate learning while maintaining a high level of accuracy. We thus explored the method of optimization by particle swarm (PSO). The results are very satisfactory (97%) compared to deep neural network with stochastic gradients descent and Nesterov accelerated Gradient optimization. The results of our work suggest the use of good descriptors in the case where the context matters little, the taking into account of the imperfection of the sensor data requires that it be used and faster models.
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Hamdi Amroun. Modèles statistiques avancés pour la reconnaissance de l’activité physique dans un environnement non contrôlé en utilisant un réseau d’objets connectés. Intelligence artificielle [cs.AI]. Université Paris-Saclay, 2018. Français. ⟨NNT : 2018SACLS406⟩. ⟨tel-02314617v1⟩

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