Geodesic
Machine learning methods for recognizing the emotional state of a telecommunications system subscriber
Informacionnye tehnologii i vyčislitelnye sistemy, no. 1 (2024), pp. 23-35.

Voir la notice de l'article provenant de la source Math-Net.Ru

Human behavior in stressful situations depends on the psychotype, socialization on a host of other factors. Phone scammers build their conversation focusing on the behavior of a certain category of people. Previously, a person is introduced into a state of acute stress, in which his further behavior to one degree or another can be manipulated. We have developed a modification of the WFT capsular neural network – 2D-CapsNet, which allowed using the photoplethysmogram (PPG) graph to identify the state of panic-stupor with an accuracy of 82%, which does not allow him to make logically sound decisions. When synchronizing a smart bracelet with a smartphone, the method allows real-time tracking of such states, which makes it possible to respond to a call from a telephone scammer during a conversation with a subscriber.
Mots-clés : robotics, artificial intelligence, neural networks, engineering, CapsNet, smart bracelet, photoplethysmogram, emotional state. 
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A. V. Osipov; A. E. Sapozhnikov; E. S. Pleshakova; S. Gataullin. Machine learning methods for recognizing the emotional state of a telecommunications system subscriber. Informacionnye tehnologii i vyčislitelnye sistemy, no. 1 (2024), pp. 23-35. https://geodesic-test.mathdoc.fr/item/ITVS_2024_1_a2/