Stress Detection Using Eye Tracking Data: An Evaluation of Full Parameters

Stress has been a common disorder in human societies and numerous studies have been conducted on the early diagnosis of stress. Previous studies have shown that it is possible to diagnose stress using eye tracking data. This study aimed to obtain a new and significant method for detecting parameters of the eye tracker and electrodermal activity signal by discrimination of “stress” vs. “relaxation” and to achieve higher accuracy than previous research. We used a Stroop task and a mathematical stressor task in which stress elements were placed in a novel design to separate stress from relaxation in the Stroop task and evaluate three levels of stress in the mathematical task. In the present study, we recorded the eye tracking data of fifteen participants and thoroughly investigated the pupil diameter (PD) and electrodermal activity (EDA) features to discriminate different stress states. After preprocessing, several features were extracted and selected. Then, the features were used for classification by applying support vector machine, linear discriminant analysis, and k-nearest neighbor classifiers. The linear discriminant analysis classifier, for which the accuracy was 88.43% in the Stroop and 91.10% in the mathematical, showed higher accuracy than the other classifiers when using PD and EDA features. Also, PD features demonstrated more reliability and ability to differentiate stress from relaxation compared to traditional EDA.