Prediction of operators cognitive degradation and impairment using hybrid fuzzy modelling

Abstract Prediction of cognitive dysfunctions in operators of human–machine systems is a complex process. The cognitive functions of attention and memory are negatively impacted in machine operation workers. Obtaining an accurate prediction of cognitive dysfunctions provides added value to better design machines and improve operator health. This research demonstrates a prediction model utilising hybrid fuzzy decision rules. The models use health indicators that measure energy imbalance of biologically active points, levels of psycho-emotional stress, fatigue and functional reserve (FR). We assess properties of attention as concentration, volume, selectivity, switchability, distribution and stability in operators of information-rich human–machine systems. Expert confidence in the obtained mathematical models exceeds the value of 0.85. The prediction quality was tested on representative control samples for the most vulnerable property of concentration of attention (CA) for this profession, and it was shown that such indicators of decision-making quality as diagnostic sensitivity, diagnostic specificity, diagnostic efficiency, predictive significance of positive and negative results exceed 0.85. The developed model proved useful for various applications in modern psychology, and psychophysiology assessment.