EOG-based drowsiness detection: Comparison between a fuzzy system and two supervised learning classifiers

Abstract

AbstractDrowsiness is a serious problem, which causes a large number of car crashes every year. This paper presents an original drowsiness detection method based on the fuzzy merging of several eye blinking features extracted from an electrooculogram (EOG). These features are computed each second using a sliding window. This method is compared to two supervised learning classifiers: a prototype nearest neighbours and a multilayer perceptron. The comparison has been carried out on a substantial database containing 60 hours of driving data from 20 different drivers. The method proposed reaches very good performances with 82% of true detections and 13% of false alarms on 20 different drivers without tuning any parameters. The best results obtained by the supervised learning classification methods are only 72% of true detection and 26% of false alarms, which is far worse than the fuzzy method. It is shown that the fuzzy method overtakes the other methods because it is able to take into account the fact that drowsiness symptoms occur simultaneously and in a repetitive way on the different features during the epoch to classify, which is of importance in the drowsiness decision-making process.