Distinguishing Drivers with Smart Glasses Data and Deep Neural Network

Driver errors are the predominant factor in road accidents. Despite efforts to standardize testing conditions, external factors inherent to real-world driving environments inherently contribute to the challenge of precise driver classification. In this study, we investigate the feasibility of classifying drivers based on experience level using physiological signals collected via smart glasses equipped with electrooculography (EOG) and inertial sensors. Our methodology involved recording real-time eye and head movement data from 30 participants - 20 experienced drivers and 10 novice drivers - while navigating a predefined 28.7 km urban and highway route under natural traffic conditions. A comprehensive signal processing pipeline was developed, including median filtering, normalization, feature extraction, and statistical analysis using ANOVA and Scheffe's method. A deep neural network classifier was then trained on the selected features, achieving an average classification accuracy of 94% across five folds. These findings demonstrate the potential of wearable sensor technologies combined with machine learning to support intelligent, non-invasive driver monitoring systems, offering personalized feedback and improving road safety.