Classification of Recorded Electrooculographic Signals on Drive Activity for Assessing Four Kind of Driver Inattention by Bagged Trees Algorithm: A Pilot Study

Abstract

The act of engaging in secondary activities while driving can cause safety risks on public roads due to the driver’s distracted attention. The objective of the research was to predict changes in driver concentration levels caused by secondary activities (eating, drinking, bending, and turning toward the rear seats) using the electrooculographic (EOG) signal. Four subjects, consisting of one male and three females between the ages of 23 and 57, performed distracting driving activities using a driving simulator. The EOG signals were recorded using JINS MEME Academic Pack smart glasses, and machine learning techniques (boosted trees, bagged trees, subspace discriminant, subspace KNN, RUSBoosted Trees) were used to classify the occurrence of secondary activities. The results show that the highest accuracy (87%) has been achieved for the bagged tree (ensemble classifier).