APPROACH TO CRITERION-BASED ASSESSMENT OF THE DEVIATION DEGREE OF THE OBJECTS FROM THE NORMAL STATE

Abstract

To develop effective methods for the timely diagnosis and forecasting of stress within a proactive management framework, it is essential to apply not only traditional but also alternative approaches. These alternative methods serve to enhance the classical assessment of an operator’s adaptive capabilities by incorporating technologies that enable the quantitative evaluation of stress levels. In the present work, an automated system has been developed specifically for monitoring the stress level of operators involved in high-responsibility processes. A key innovation of this system is the integration of ChatGPT functionality to analyze and synthesize existing methodologies for evaluating the psychological dimensions of stress. The inclusion of AI-driven analysis enables more precise recognition of subtle psychological indicators, contributing to a more holistic understanding of stress dynamics. The proposed automated system calculates the operator’s current stress level while also considering the depletion of the body's regulatory mechanisms. This approach enables proactive planning of technological workflows to mitigate operator fatigue and prevent the breakdown of adaptive responses during task performance. Such breakdowns can potentially trigger adverse events, especially in high-stakes domains like aviation. By combining physical and psychological assessment techniques, the system supports the development of a unique, data-driven model for stress evaluation. This model significantly improves the reliability of estimating the adaptive reserves of the human body, thereby contributing to the overall safety and efficiency of human-centered technological systems.