This article is cited in 1 paper

DATA PROCESSING AND ANALYSIS

Machine learning methods for recognizing the emotional state of a telecommunications system subscriber

A. V. Osipov^ab, A. E. Sapozhnikov^a, E. S. Pleshakova^ab, S. Gataullin<sup>abc

a</sup> Financial University under the Government of the Russian Federation, Moscow
^b MIREA — Russian Technological University, Moscow
^c Central Economics and Mathematics Institute of the Russian Academy of Sciences, Moscow

Abstract: 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.

Keywords: robotics, artificial intelligence, neural networks, engineering, CapsNet, smart bracelet, photoplethysmogram, emotional state.

DOI: 10.14357/20718632240103

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