EmoTracker: Emotion Recognition between Distance Learning and Special Educational Needs

Diletta Chiusaroli, Pio Alfredo Di Tore


The work documents the development of EmoTracker, a software that uses an artificial intelligence algorithm for the recognition of the level of attention and for the recognition of students' emotions during video meetings. The software uses the video stream from the webcam as input and is able to detect the user's age and gender, the level of attention, based on eye movements, and the six Basic Emotions identified in the Basic Emotion Theory. The software is based on the MorphCast Software Development Kit, which combines machine learning and face recognition with analysis of sex, age and emotions to estimate the level of attention of users, without using personal data. Currently, the software is in the testing phase, by the students of the Technologies for Inclusion course at the University of Cassino. As soon as it has reached full reliability and stability, the software will be used in the activities of the CA.SP.I. (Special career for inclusion)


Special Educational Needs; Emotion Tracking; Distance Learning.

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DOI: https://doi.org/10.32043/gsd.v4i4_sup.239


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