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.

Drowsiness while driving is one of the main causes of fatal accidents, especially on monotonous routes such as highways. The goal of this paper is to design a completely standalone, distraction-free, and wearable system for driver drowsiness detection by incorporating the system in a smartwatch. The main objective is to detect the driver's drowsiness level based on the driver behavior derived from the motion data collected from the built-in motion sensors in the smartwatch, such as the accelerometer and the gyroscope. For this purpose, the magnitudes of hand movements are extracted from the motion data and are used to calculate the time, spectral, and phase domain features. The features are selected based on the feature correlation method. Eight features serve as an input to a support vector machine (SVM) classifier. After the SVM training and testing, the highest obtained accuracy was 98.15% (Karolinska sleepiness scale). This user-predefined system can be used by both left-handed and right-handed users, because different SVM models are used for different hands. This is an effective, safe, and distraction-free system for the detection of driver drowsiness.

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Studies have shown that a high precision driver alertness monitoring system is an essential and a monetary countermeasure to reduce the road accidents. This paper presents a novel approach to measure the driver alertness, evaluated by a smartwatch device based on fusion of direct and indirect method. The driver chronic physiological state is monitor by adopting a photoplethysmography sensor on the driver finger that is connected to a wrist-type wearable device. A Bluetooth Low Energy module connected to the wearable device transmits the PPG data to the smartwatch in real-time. Meanwhile, the indirect method, driver steering wheel movement can be derived by utilizing the motion sensors integrated in the smartwatch which include a tri-axis accelerometer and a gyroscope sensors. The respiration signals can be derived from the PPG time- and frequency-domains attributes. The data obtained from both methods aforementioned are subsequently decomposed into relevant features in time, spectral context and phase space domain, and thus computes the alertness index. Here, the correlations between the extracted features and the subjective Koralinska Sleepiness Scale are studied as well along with the recorded experimental videos. This study reveals that the alertness index prediction accuracy can be reached up to 96.3% based on the descriptive extracted features.