The method of dynamic detection of PC operator fatigue based on eye movement characteristics

The onset of fatigue is dangerous in areas of activity that require high concentration of human attention, such as air traffic controllers, nuclear power plant operators, etc. It should be noted that these types of activities are characterized by the fact that most of the time the employee sits at the workplace and his gaze is directed at the monitor. The paper presents a method for dynamic detection of PC operator fatigue based on eye movement characteristics. The method for dynamic detection of fatigue implements a training scenario for a fatigue detection model and a fatigue detection scenario. Within the training scenario, eye movement characteristics are calculated and correlations with fatigue test results are searched for. Within the fatigue detection scenario, eye movement characteristics that most strongly correlate with fatigue are selected. These characteristics can also be divided by the types of physical events on which they are based. We can distinguish such characteristics as speed, time, quantity, size, percentage, frequency and ratio characteristics. To find correlations between eye movement characteristics and fatigue, a dataset of eye movements and the results of tests and questionnaires such as the go/no-go task, the Landolt ring test, and the VAS-F questionnaire were analyzed. The dataset consists of gaze coordinate recordings from 15 participants acting as PC operators. To determine the degree of fatigue, the participant completed the VAS-F questionnaire. The Landolt ring correction test is a test used to measure attention concentration. The labeled dataset is used to train a machine learning model that detects fatigue. The experimental results showed that using the characteristics selected in the study yielded the most promising results. This approach allowed us to achieve the highest F-measure and the best average accuracy, indicating the overall reliability of the model.