Drowsiness Detection Research Articles

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

Detection of human drowsiness by the vestibulo-ocular reflex compensating for heartbeat-induced head perturbations

The vestibulo-ocular reflex (VOR) stabilizes vision during head movements by counter-rotating the eyes in the orbits. Although considered one of the simplest reflexes due to its minimal neuronal circuity comprising a 3-neuron arc, previous studies have shown that VOR performance deteriorates in both monkeys and humans when they are drowsy. Given constant head perturbations under dynamic environments, the VOR has been proposed as a viable biomarker for detecting human drowsiness in automobiles and other moving vehicles. However, under stationary environments where exogenous head movements are absent, its applicability has been questioned. In this study, we demonstrate that each heartbeat generates small yet distinctive head movements, and the VOR compensates for these minor head perturbations. Furthermore, we show that the effectiveness of VOR responses varies with the degree of drowsiness, indicating that the VOR can serve as an indicator of drowsiness, even in stationary contexts such as in classrooms and offices.

The Dynamics of Heart Rate Asymmetry and Situational Sleepiness from Evening to Night: The Role of Daytime Sleepiness.

The relationship between daytime sleepiness and heart rate asymmetry (HRA) during the transition from evening to night is crucial for understanding autonomic regulation and its implications for alertness. This study aims to investigate how daytime sleepiness influences HRA dynamics from evening to night and how situational sleepiness correlates with HRA metrics. HRA metrics were calculated at 8 P.M., 9 P.M., and 10 P.M. in 50 participants, categorized into 'Lower Normal' and 'Higher Normal' daytime sleepiness groups based on Epworth Sleepiness Scale (ESS) scores. Situational sleepiness was assessed using the Karolinska Sleepiness Scale (KSS) and Stanford Sleepiness Scale (SSS). The results demonstrated that individuals with 'Higher Normal' daytime sleepiness exhibited lower HRA metrics at 10 P.M. compared to those with 'Lower Normal' daytime sleepiness, supporting the suggestion that higher daytime sleepiness correlates with reduced parasympathetic activity and diminished autonomic responsiveness. Significant negative correlations between situational sleepiness and HRA metrics were observed in the 'Higher Normal' group, particularly with the SSS. Therefore, increased daytime sleepiness affects HRA dynamics by decreasing parasympathetic activity and altering autonomic regulation at the beginning of the biological night (10 P.M.). These findings suggest potential applications for enhancing drowsiness detection and managing fatigue in safety-critical environments.

A multi-aware graph convolutional network for driver drowsiness detection

Driver drowsiness is a leading cause of traffic accidents. Utilizing deep neural networks, facial feature-based methods have achieved promising results in drowsiness detection. However, these methods suffer from two limitations. Firstly, they only focus on features from one or two facial regions, thus overlooking the relationships between features across different facial regions. Secondly, these methods struggle to account for individual driver variability, a common phenomenon where drivers may display dissimilar signs of drowsiness. These limitations lead to inaccurate drowsiness detection. To address these issues, in this paper we propose a multi-aware graph convolutional network (MAGCN). At the heart of MAGCN are two feature extractors: the class- and attention-aware extractor (CAAE), and the composite temporal-aware extractor (CTAE). The CAAE explores interdependencies within global and local facial features, while the CTAE leverages temporal information to capture dynamic changes in features. Moreover, a task-oriented graph convolutional network is designed to refine the drowsiness feature space for precise detection. Experiment results show that the proposed MAGCN exhibits competitive detection performance, when compared with state-of-the-art drowsiness detection approaches on two public datasets. In summary, the proposed model not only learns and analyzes correlations between features from various facial regions, but also tackles individual driver variability.

An optimized deep hybrid learning for multi-channel EEG-based driver drowsiness detection

Driver drowsiness is a severe issue that has contributed to numerous fatal accidents and injuries. Thus, detecting driver drowsiness is an important task that has been the subject of intensive research in recent years. There have been numerous proposed physiological signals for detecting driver drowsiness. However, the Electroencephalographic (EEG) signal is the most commonly used due to its direct relationship with drowsiness and its simplicity of acquisition. Recently, different Machine Learning (ML) and Deep Learning (DL) models have been proposed to detect driver drowsiness. This study utilized a publicly accessible dataset containing twelve healthy participants. Reading numerous research papers, we determined no specific EEG-based drowsiness preprocessing parameter values. Consequently, as a first step, and for the first time, to our knowledge in this field, we applied an optimization algorithm to determine the optimal preprocessing parameter values using a CNN model and accuracy as the objective function. The obtained results demonstrated the importance of selecting the correct values, as the mean accuracy score increased from 91% before optimization to 95% after optimization for the proposed CNN. The training time has been reduced. Also, as a second step, we have used the Optuna Hyperparameter optimization framework to select the optimal CNN Hyperparameters, which increased the mean accuracy from 95% to 97%. Finally, to take advantage of Deep Hybrid learning, we have fused the CNN “features extractor” with seven ML classifiers, with the CNN-SVM classifier achieving the highest average accuracy of 99.9%, and the training time has been reduced to a shallow value.

Non-Intrusive Drowsiness Detection for Accident Prevention Using a Novel CNN

Non-Intrusive Drowsiness Detection for Accident Prevention Using a Novel CNN

A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio.

Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this context, one solution to address the challenge of drowsiness detection is to anticipate drowsiness by alerting drivers in a timely and effective manner. Thus, this paper presents a Convolutional Neural Network (CNN)-based approach for drowsiness detection by analyzing the eye region and Mouth Aspect Ratio (MAR) for yawning detection. As part of this approach, endpoint delineation is optimized for extraction of the region of interest (ROI) around the eyes. An NVIDIA Jetson Nano-based device and near-infrared (NIR) camera are used for real-time applications. A Driver Drowsiness Artificial Intelligence (DD-AI) architecture is proposed for the eye state detection procedure. In a performance analysis, the results of the proposed approach were compared with architectures based on InceptionV3, VGG16, and ResNet50V2. Night-Time Yawning-Microsleep-Eyeblink-Driver Distraction (NITYMED) was used for training, validation, and testing of the architectures. The proposed DD-AI network achieved an accuracy of 99.88% with the NITYMED test data, proving superior to the other networks. In the hardware implementation, tests were conducted in a real environment, resulting in 96.55% and 14 fps on average for the DD-AI network, thereby confirming its superior performance.

A novel deep learning‐based technique for driver drowsiness detection

AbstractEvery year, many people lose their lives because of road accidents. It is evident from statistics that drowsiness is one of the main causes of a large number of car accidents. In our research, we wish to solve this major problem by measuring the drowsiness level of the human brain while driving. The study aims to develop a novel technique to detect different alertness levels (i.e., awake, moderately drowsy, and maximally drowsy) of a person while driving. A hybrid model using a stacked autoencoder and hyperbolic tangent Long Short‐Term Memory (TLSTM) network with attention mechanism is designed for this purpose. The designed model uses different biopotential signals, such as electroencephalography (EEG), facial electromyography (EMG), and different biomarkers, such as pulse rate, respiration rate galvanic skin response, and head movement to detect a person's alertness level. Here, the stacked autoencoder model is used for automated feature extraction. TLSTM is used to predict a person's alertness level using stacked autoencoder network‐extracted features. The proposed model can classify awake, moderately drowsy, and maximally drowsy states of a person with accuracies of 99%, 98.3%, and 98.6%, respectively. The novel contributions of the paper includes (i) incorporation of an attention mechanism into the TLSTM network of the proposed hybrid model to focus on the emphatic states to enhance classification accuracy, and (ii) utilization of EEG, facial EMG, pulse rate, respiration rate, galvanic skin reaction, and head movement pattern to assess a person's alertness level.

Low-Cost Wearable Sleepiness Detection Based on Heart Rate Reduction

Low-Cost Wearable Sleepiness Detection Based on Heart Rate Reduction

NGHIÊN CỨU XÂY DỰNG HỆ THỐNG THEO DÕI, GIÁM SÁT TRẠNG THÁI BUỒN NGỦ DỰA TRÊN THỊ GIÁC MÁY TÍNH

This article focuses on the research and development of a model for a drowsiness detection and monitoring system while driving based on computer vision. The product can be applied to current vehicles (such as cars or vehicles with similar functions). The study utilizes several state-of-the-art sleep detection models to collect and process data and train the model to provide quantitative results. The model is evaluated through this process, and improvements are proposed to make it suitable for vehicles in Vietnam. Experimental results show that the system can capture images via a camera, detect human faces, predict sleep states, and send alerts to a server, displaying warning data on a website interface. Essential evaluations indicate that the system operates stably and relatively accurately according to the pre-established design and programming requirements. The results of this research can be applied in practical scenarios or serve as a practical model for students in artificial intelligence, robotics, and automation control fields to enhance their ability to apply theoretical knowledge to real-world problem-solving. Keywords: Drowsiness; traffic; smart robots; computer vision; artificial intelligence.

Enhancing Automative Safety System for Driver Fatigue Detection by Using Mediapipe’s Framework

Traffic accidents caused by tiredness, exhaustion, and distracted driving are a big concern on a global scale. As per global Road Safety reports drowsy driving was reason for 91,000 road accidents in Tamil Nadu in the year 2023. In this study, I suggest a collective deep learning architecture for automatic driver sleepiness detection while some earlier works are suggested extracting the lips and eyes movements to identify sleepiness, more contemporary computer vision-based systems have performed only somewhat well. This is because they either use extremely large deep learning models with still-poor performance or they use hand-crafted features with traditional methods like K-NN Algotithm and openCV. To overcome such issues in this project I am planing to create a Driver Drowsiness Detection and Alerting System using Mediapipe‘s Face Mesh solution in Python. These systems assess the driver‘s alertness and warn the driver if needed. Continuous driving can be tedious and exhausting. A motorist may get droopy and perhaps nod off due to inactivity. we will create a drowsy driver detection system to address such an issue. For this, we will use Mediapipe‘s Face Mesh solution in python and the Eye Aspect ratio formula using by Navie Bayes Algorithm. My goal is to create a robust and easy-to-use application that detects and alerts users if their eyes are closed for a long time. Keyword: Navie Baye’s, Mediapipe Framework.

A Review on Deep Learning Techniques for EEG-Based Driver Drowsiness detection systems

A Review on Deep Learning Techniques for EEG-Based Driver Drowsiness detection systems

Multi-Index Driver Drowsiness Detection Method Based on Driver's Facial Recognition Using Haar Features and Histograms of Oriented Gradients.

It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver's state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection.

Data fusion for driver drowsiness recognition: A multimodal perspective

Drowsiness is characterized by decreased alertness and an increased inclination to fall asleep, typically from factors such as fatigue, sleep deprivation, or other related influences. In the context of driving, drowsiness poses substantial safety risks. The detection of driver drowsiness is of paramount importance in ensuring road safety, contributing to a significant number of accidents worldwide. Utilizing AI for drowsiness detection offers a potent solution to enhance road safety by identifying driver fatigue and preventing potential accidents. The proposed system addresses the challenge of detecting driver drowsiness using a WACHSens dataset collected from both manual and automated driving modes, encompassing rested and fatigued states. Various data sources, including vehicle-related information, facial expressions, and bio signals are employed to create a robust drowsiness detection system. A novel approach that leverages Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to effectively detect drowsiness in drivers and achieve a 96 % accuracy level. It helps in enhancing road safety by devising effective drowsiness detection mechanisms, potentially preventing accidents and saving lives. Recall, accuracy, f1-score, and precision are the performance metrics to measure the drowsiness condition.

The activity of different brain regions in fatigued and drowsy drivers: A systematic review based on EEG findings

Introduction: Drowsiness and fatigue during driving is one of the major causes of traffic accidents, morbidity, and mortality in societies. Known electroencephalography (EEG) as a gold standard in fatigue detection, this study aims to determine the alterations in different brain regions in fatigued and drowsy drivers. Methods: The databases PubMed, Scopus, Web of Science, and Embase have been systematically searched for published studies until September 1, 2023. Also, the references of the relevant articles have been searched manually. Reporting the quality assessment of the studies has been done by the Joanna Briggs Institute (JBI) critical appraisal tool for experimental studies. The study purpose was achieved using appropriate statistical methods. Results: Sixty-five eligible studies consisting of 1450 participants were included. The most common age and gender between participants is young males. The majority of the studies were held in high-income countries. Simulated car driving studies are utilized frequently in the studies. Common brain-wave changes seen in various regions include increased alpha in the occipital area (51.7%) and decreased alpha in the frontal region (eight out of eight simulated driving studies), decreased beta in the frontal region (25.8% of driving simulated studies), increased theta in the frontal region (36.2%), and decreased gamma in the central and temporal areas. Delta waves increase throughout the brain in an equal proportion. Conclusion: The occipital alpha increase, frontal beta decrease, frontal theta increase, and central and temporal gamma decrease, are frequent findings among the studies reporting fatigue and drowsiness. Based on the discrepancies and inconsistencies reported in EEG results, the detection of fatigue and drowsiness in the driving task via EEG data should be done with caution.

Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving

AbstractIn response to the global safety concern of drowsiness during driving, the European Union enforces that new vehicles must integrate detection systems compliant with the general data protection regulation. To identify drowsiness patterns while preserving drivers’ data privacy, recent literature has combined Federated Learning (FL) with different biosignals, such as facial expressions, heart rate, electroencephalography (EEG), or electrooculography (EOG). However, existing solutions are unsuitable for drowsiness detection where heterogeneous stakeholders want to collaborate at different levels while guaranteeing data privacy. There is a lack of works evaluating the benefits of using Hierarchical FL (HFL) with EEG and EOG biosignals, and comparing HFL over traditional FL and Machine Learning (ML) approaches to detect drowsiness at the wheel while ensuring data confidentiality. Thus, this work proposes a flexible framework for drowsiness identification by using HFL, FL, and ML over EEG and EOG data. To validate the framework, this work defines a scenario of three transportation companies aiming to share data from their drivers without compromising their confidentiality, defining a two-level hierarchical structure. This study presents three incremental Use Cases (UCs) to assess detection performance: UC1) intra-company FL, yielding a 77.3% accuracy while ensuring the privacy of individual drivers’ data; UC2) inter-company FL, achieving 71.7% accuracy for known drivers and 67.1% for new subjects, ensuring data confidentiality between companies but not intra-organization; and UC3) HFL inter-company, which ensured comprehensive data privacy both within and between companies, with an accuracy of 71.9% for training subjects and 65.5% for new subjects.

An automated and highly efficient driver drowsiness detection and alert system using electroencephalography signals for safe driving

An automated and highly efficient driver drowsiness detection and alert system using electroencephalography signals for safe driving

A systematic review on analysis of automatic drowsiness detection for preventing road accidents

A systematic review on analysis of automatic drowsiness detection for preventing road accidents

Driver Drowsiness Detection using MATLAB

Driver drowsiness is critical toroad accidents and fatalities worldwide. Various systems utilizing image processing, computer vision, and machine learning techniques have been proposed to address this. This paper consolidates the findings from few relevant studies focusing on drowsiness detection in drivers. The proposed systems aim to detect signs of drowsiness, such as eye closure and facial expressions, and issue timely alerts to prevent accidents. By analyzing these studies, thispaper provides insights into the methodologies, challenges, and advancements in drowsiness detection technology, paving the way for more robust and effective systems in the future.

Video-Based Driver Drowsiness Detection With Optimised Utilization of Key Facial Features

Video-Based Driver Drowsiness Detection With Optimised Utilization of Key Facial Features