The detection of alcohol intoxication using electrooculography signals from smart glasses and machine learning techniques

Abstract

The operation of a motor vehicle under the influence of alcohol poses a significant risk to the safety of the driver, passengers, and other road users. Electrooculographic (EOG) signal analysis can be used to understand the movements and behavior of the eyes while driving. In our study, we used smart glasses to collect EOG data from nine participants who used a driving simulator. Their level of alcoholic intoxication was simulated by drunk vision goggles at three different levels of inebration (0, 1, 2, and 3‰ blood alcohol content). We used machine learning algorithms (decision trees, support vector machines, nearest-neighbor classifiers, boosted trees, bagged trees, subspace discriminant classifier, subspace k nearest-neighbor classifier, and RUSBoosted Trees) to analyze the data. The Bagged Trees achieved the highest accuracy of 79%. The most important features to detect simulated alcohol intoxication were the blink rate and the velocity of the saccade, a rapid simultaneous movement of both eyes in the same direction. Our study shows the potential of using smart glasses and machine learning for the automated detection of alcohol intoxication, even when alcohol consumption is simulated.