Identification of the Psychophysiological State of the User Based on Hidden Monitoring in Computer Systems

Abstract

It is found that the features of the voice, keystroke dynamics and pattern of a subject’s use of a computer mouse contain the following information about the psychophysiological state of the operator: normal, fatigue, intoxication, excited, and relaxed (sleepy). Voice features are the best for identifying fatigue or a sleepy state of a speaker. Keystroke dynamics, aside from these states, have features that characterize the normal state of the operator. Some features of working with a computer mouse contain information about the states of intoxication and sleepiness. This experiment on state identification was based on Bayes strategies and the neural network approach; the best result was a 5.9% error in determining the state when monitoring a subject for no more than 100 s.