Fatigue detection caused by office work with the use of EOG signal

Although the psychophysiological signs of fatigue are well known, automatic methods for the detection of fatigue in employees in specific working conditions are still lacking. Many people do repetitive work on computers and become fatigued; therefore, the detection of fatigue in employees can help prevent accidents and increase their work efficiency. In this article, we propose an algorithm for the effective detection of fatigue which is based only on electrooculographic (EOG) signal. Three features were assessed: blink duration, blink amplitude, and time between blinks. To cause fatigue, the {N} -back test, lasting for 60 minutes, was carried out. The article presents the research results for 24 users. The effectiveness of the proposed system was measured by the accuracy of classification. The average classification accuracy was 0.93 for user-dependent mode and 0.89 for user-independent mode. The results of the conducted experiments indicated that assessing the three proposed features can help in the effective detection of fatigue in users.