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Chapitre D'ouvrage Année : 2021

Emotion Detection Based on Smartphone Using User Memory Tasks and Videos

Résumé

In this paper, we present a research study on the classification of emotions, through data gathered on a smartphone. To this end, we have developed a mobile application to elicit emotions in participants using memory tasks with success – failure manipulation and also using video clips. Interactions were recorded with accelerometer and gyroscope sensors records and keystroke on the device. We trained supervised classification models, with the records, to predict the nature of emotion elicited on two dimensions (pleasure and activation) and the success or failure related tasks memory tasks. In order to evaluate the emotion induction we have proposed a self-assessment procedure. We achieved interesting results on the pleasure dimension, by proposing a protocol with natural interactions on smartphone.
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Dates et versions

hal-02972664 , version 1 (20-10-2020)

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Citer

Nicolas Simonazzi, Jean-Marc Salotti, Caroline Dubois, Dominique Seminel. Emotion Detection Based on Smartphone Using User Memory Tasks and Videos. Tareq Ahram; Redha Taiar; Karine Langlois; Arnaud Choplin. Advances in Intelligent Systems and Computing 1253, Human Interaction, Emerging Technologies and Future Applications III, proceedings of the 3rd International Conference on Human Interaction and Emerging Technologies: Future Applications (IHIET 2020), August 27–29, 2020, Paris, France, 1253, Springer, pp.244-249, 2021, AISC, ⟨10.1007/978-3-030-55307-4_37⟩. ⟨hal-02972664⟩
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