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Story Generation from Smart Phone Data : A script approach

Abstract : Script is a structure describes an appropriate sequence of events or actions in our daily life. A story, is invoked a script with one or more interesting deviations, which allows us to deeper understand about what were happened in routine behaviour of our daily life. Therefore, it is essential in many ambient intelligence applications such as healthmonitoring and emergency services. Fortunately, in recent years, with the advancement of sensing technologies and embedded systems, which make health-care system possible to collect activities of human beings continuously, by integrating sensors into wearable devices (e.g., smart-phone, smart-watch, etc.). Hence, human activity recognition (HAR) has become a hot topic interest of research over the past decades. In order to do HAR, most researches used machine learning approaches such as Neural network, Bayesian network, etc. Therefore, the ultimate goal of our thesis is to generate such kind of stories or scripts from activity data of wearable sensors using machine learning approach. However, to best of our knowledge, it is not a trivial task due to very limitation of information of wearable sensors activity data. Hence, there is still no approach to generate script/story using machine learning, even though many machine learning approaches were proposed for HAR in recent years (e.g., convolutional neural network, deep neural network, etc.) to enhance the activity recognition accuracy. In order to achieve our goal, first of all in this thesis we proposed a novel framework, which solved for the problem of imbalanced data, based on active learning combined with oversampling technique so as to enhance the recognition accuracy of conventional machine learning models i.e., Multilayer Perceptron. Secondly, we introduce a novel scheme to automatically generate scripts from wearable sensor human activity data using deep learning models, and evaluate the generated method performance. Finally, we proposed a neural event embedding approach that is able to benefit from semantic and syntactic information about the textual context of events. The approach is able to learn the stereotypical order of events from sets of narrative describing typical situations of everyday life.
Keywords : Story Data
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Submitted on : Thursday, July 23, 2020 - 2:21:42 PM
Last modification on : Wednesday, October 14, 2020 - 4:18:58 AM


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  • HAL Id : tel-02905464, version 1




Trung Ky Nguyen. Story Generation from Smart Phone Data : A script approach. Artificial Intelligence [cs.AI]. Université Grenoble Alpes, 2019. English. ⟨NNT : 2019GREAS030⟩. ⟨tel-02905464⟩



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