Electroencephalography-based sleepiness detection system (ESDS) is a brain-computer interface that evaluates a driver’s sleepiness level directly from cerebral activity. The goals of ESDS research are to estimate and produce a timely warning to prevent declines in performance efficiency and to inhibit sleepiness-related accidents. We first, review different types of measures used in sleepiness detection systems (SDSs) and presents their advantages and drawbacks. Second, the review includes several techniques proposed in ESDSs to optimize the number of EEG electrodes, increasing the sleepiness level resolution and incorporation of circadian information. Finally, the review discusses future direction that can be considered in the development of ESDS.

A Review on EEG-Based Automatic Sleepiness Detection Systems for Driver

Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

https://www.mdpi.com/1424-8220/21/5/1734/pdf

Convolutional Neural Network for Drowsiness Detection Using EEG Signals.

Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

https://www.mdpi.com/1424-8220/20/4/1029/pdf

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection.

The development of detection methodologies for reliable drowsiness tracking is a challenging task requiring both appropriate signal inputs and accurate and robust algorithms of analysis. The aim of this research is to develop an advanced method to detect the drowsiness stage in electroencephalogram (EEG), the most reliable physiological measurement, using the promising Machine Learning methodologies. The methods used in this paper are based on Machine Learning methodologies such as stacked autoencoder with softmax layers. Results obtained from 62 volunteers indicate 100% accuracy in drowsy/wakeful discrimination, proving that this approach can be very promising for use in the next generation of medical devices. This methodology can be extended to other uses in everyday life in which the maintaining of the level of vigilance is critical. Future works aim to perform extended validation of the proposed pipeline with a wide-range training set in which we integrate the photoplethysmogram (PPG) signal and visual information with EEG analysis in order to improve the robustness of the overall approach.

An Innovative Deep Learning Algorithm for Drowsiness Detection from EEG Signal

Deaths and injuries resulting from road traffic crashes remain a serious problem globally and current trends suggest that this will continue to be the case in the foreseeable future. In this paper, we propose a reliable method towards drowsiness detection by analyzing images of the driver’s face. In fact, the shadows caused by wearing glasses and/or bad light conditions may, in particular, decrease the accuracy rate of the blinking detection. In addition, drowsiness symptoms are not just restricted to the frequency of blinking, as most of existing works do, but also include yawning and nodding. Thus, instead of using a single facial feature to predict the drowsiness state, we jointly combine eye closure and yawning by measuring the eye and the mouth aspect ratios, and head pose which is estimated by analyzing the optical flow. Then, we investigate the multilayer perceptron and the K-nearest neighbors as two classification techniques. The proposed method has been tested on the challenging private NTHU-DDD benchmark video dataset, and the preliminary experimental results show the effectiveness of the proposed automated method.

Driver Drowsiness Detection Based on Joint Monitoring of Yawning, Blinking and Nodding

The paper presents a study regarding the possibility to develop a drowsiness detection system for car drivers based on three types of methods: EEG and EOG signal processing and driver image analysis. In previous works the authors have described the researches on the first two methods. In this paper the authors have studied the possibility to detect the drowsy or alert state of the driver based on the images taken during driving and by analyzing the state of the driver's eyes: opened, half-opened and closed. For this purpose two kinds of artificial neural networks were employed: a 1 hidden layer network and an autoencoder network.

https://iopscience.iop.org/article/10.1088/1757-899X/252/1/012097/pdf

Driver drowsiness detection using ANN image processing

The paper presents a study regarding the possibility to develop a drowsiness detection system for car drivers based on three types of methods: EEG and EOG signal processing and driver image analysis. In previous works the authors have described the researches on the first two methods. In this paper the authors have studied the possibility to detect the drowsy or alert state of the driver based on the images taken during driving and by analyzing the state of the driver’s eyes: opened, half-opened and closed. For this purpose two kinds of artificial neural networks were employed: a 1 hidden layer network and an autoencoder network.

/pdf/researches-regarding-the-continuous-improvement-of-the-abs-2l6dyqbk0p.pdf

Researches Regarding the Continuous Improvement of the ABS (Anti-Lock Braking System) Operation for the Passenger Cars

The paper presents a new method of monitoring and warning drowsy drivers in order to prevent accidents. Analyzing current approaches we decided to study algorithms which can be used for drowsiness/awareness detection when applying techniques based on EEG and EMG signals measurement. There are also a large number of studies which uses EEG signals for brain – computer interface applications. This method has the advantage that is independent from the equipment, vehicle or situ where it is used, and can be applied both for drivers and equipment operators too.

Vehicle Driver Drowsiness Monitoring and Warning System

The paper presents the development of a multi-criterial fuzzy decision algorithm applied to the monitoring and warning of drowsy drivers in order to prevent accidents, based on EEG signals and eye state images. The drowsiness warning system is based on four main components, which learn, analyze and decide whether the driver is or is not in a drowsy or sleepy state. This result can be then used to warn the driver if he/she is in a drowsy state.

Fuzzy Decision Algorithm for Driver Drowsiness Detection

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Driver drowsiness detection using ANN image processing

Driver drowsiness detection using ANN image processing

Researches Regarding the Continuous Improvement of the ABS (Anti-Lock Braking System) Operation for the Passenger Cars

Vehicle Driver Drowsiness Monitoring and Warning System

Fuzzy Decision Algorithm for Driver Drowsiness Detection