Abstract
The detection of a state of non-concentration would allow a system to suggest that workers take breaks to recover their concentration and avoid human errors. Eye gaze information can contribute to the objective analysis of human mental states, such as the concentration state. In this study, we explored a pipeline for constructing machine learning models to recognize the state of concentration using eye-gaze data during reading. The following three stages were included in the evaluation: 1) parameter adjustment, 2) estimation of performance differences in the feature group, and 3) clarification of the limitations of the classification performance. The results show that the classification model performance achieved a maximum F1-score of 0.933, suggesting that Random Forest and a 12-s window size are effective as parameters. This study is expected to contribute to the development of an application for detecting a non-concentration state.
https://ror.org/00qg0kr10 
