Informational Models of Machine Education Learning: Systems for Safe Diagnosis of Emotional and Psychological States

Abstract

The purpose of the study is to increase the functional efficiency of a diagnostic decision support system capable of machine engineering learning to determine the emotional and mental state of a person from an image of his face. The object of the study is information models of machine engineering learning. The scientific task is to increase the accuracy and efficiency of assessing the current level of stability and the influence of stress, fatigue and other factors through modern models of machine engineering learning. The methodology involves the use of IEI (information-extreme intelligent technology) technology and functional analysis methods to construct categories of machine engineering learning models. As a result, functional category models of information-extreme machine engineering learning were proposed using the basic method of IEI technology and optimization of the system of control tolerances, reflecting the transformations of information and information flows occurring during the cognitive processes of formation and decision-making. These models will be used to determine the emotional and psychological state of a person under arbitrary initial conditions for performing individual diagnostic actions. The most important limitation regarding our study, in our opinion, is the dependence of accuracy in diagnosing an emotional and psychological state on the quality of the images subject to analysis. Using only facial images may not take into account other important aspects of nonverbal communication such as gestures and posture, which may reduce the overall effectiveness of the system. Taking into account certain limitations, we have identified a number of promising areas for further research. Thus, in future studies it is planned to include the analysis of video recordings and other sources that allow us to analyze the emotional spectrum of a person. In addition, it is planned to add biometric research methods to the study.