Smartwatch-Based Wearable EEG System for Driver Drowsiness Detection

Abstract

Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Many physiological signals have been proposed to detect driver drowsiness. Among these signals, Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many EEG-based driver drowsiness detection (DDD) models gained more and more attention in recent years. However, one limitation of these studies is that these models merely estimate discrete labels and thus did not allow for estimating relative severity of driver drowsiness. This study proposes Support Vector Machine based Posterior Probabilistic Model (SVMPPM) for DDD aimed at transforming the drowsiness level to any value of 0~1 instead of discrete labels. A fully wearable EEG system which consists of a Bluetooth-enabled EEG headband and a commercial smartwatch was used to evaluate the proposed model in real-time way. Twenty subjects who participated in one-hour monotonous driving simulation experiment were used to develop this model with fifteen subjects for building model and five subjects for testing model. According to a video-based reference, the proposed system obtained an accuracy of 91.25% accuracy for alert group (73 out of 80 datasets), 83.78% accuracy for early-warning group (93 out of 111 datasets) and 91.92% accuracy for full-warning group (91out of 99 datasets). These results indicate that the combination of proposed SVMPPM, EEG headband and wrist-worn smart device constitutes an effective, simple, and inexpensive wearable solution for DDD.Index Terms-Driver drowsiness detection, EEG, wearable devices, smartwatch, support vector machine.

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