Real-time Drowsiness Detection Research Articles

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea.

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta-alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta-alpha-ratio (87.2%) and delta-theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta-alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

DRIVER DROWSINESS DETECTION SYSTEMS

Car accidents are frequently caused due to many reasons like drowsiness, drunkenness or tiredness, which has serious consequences for traffic safety. Numerous factors, such as fatigue, intoxication, or drowsiness, commonly contribute to car accidents, which have detrimental effects on traffic safety. Many fatal collisions might be avoided if sleepy drivers were warned beforehand. Many sleepiness detection technologies are available to identify and warn drivers of any indications of inattention while driving. Sensors in self-driving cars must be able to detect whether a driver is sleepy, agitated, or experiencing sharp emotional fluctuations. Thus far There have been numerous recommendations for enhanced warning systems to prevent sleepy driving. When these systems detect a significant lane departure, a change in the headway distance, or other indications of subpar driving, they sound an alarm. These systems rely heavily on machine learning algorithms, which allow them to learn and adjust to unique driving patterns and characteristics. In supervised learning, models are trained on labelled datasets to discriminate between awake and sleepy states. These approaches are widely used for classification tasks. Unsupervised learning techniques are also applied for anomaly detection, which finds departures from typical driving behaviour. The incorporation of cutting-edge sensors like facial recognition cameras and brainwave sensors, which offer more thorough insights into the driver's condition, has recently improved driver sleepiness detection systems. Moreover, these systems are more accurate and responsive when real-time data processing and cloud-based analytics are used. KEYWORDS Drowsiness-detection, Driver-Fatigue, Computer Vision, Real Time, survey, Eye Tracking Technology, Automotive Safety Systems, Driver Assistance Systems, Real-time Drowsiness Detection, Alertness Monitoring.

Advancements in Drowsiness Detection: A Review

This review study looks at current advancements in sleepiness detection technologies to address the urgent problem of driving while sleepy leading to accidents. In order to prevent future accidents, it analyzes several approaches and technology and emphasizes how crucial it is to correctly recognize drowsy conditions in drivers. Important results highlight the effectiveness of methods for real-time drowsiness detection such as convolutional neural networks (CNNs), eye aspect ratio (EAR) analysis, and deep learning integration. Additionally, improvements in computer vision algorithms make it possible to precisely analyze facial landmarks and eye blinking patterns, which improves the accuracy of detection systems. In order to protect driver comfort and system reliability, the evaluation emphasizes the importance of non-intrusive fatigue detection techniques while also emphasizing the necessity to reduce false warnings. Remarkably, certain models detect drowsy-eye with an accuracy of above 95%. All things considered, the review emphasizes how important cutting-edge technology is in reducing the risks related to drunk driving, especially deep learning and computer vision algorithms. It promotes the deployment of reliable sleepiness detection technologies in order to increase road safety and reduce accidents brought on by fatigued drivers. Key Words: Drowsiness detection, Eye blink duration, Facial landmark detection, Deep learning algorithms, Computer vision, Eye aspect ratio, Road safety.

Utilizing commercial off-the-shelf smartwatches in a real-time drowsiness detection system

Utilizing commercial off-the-shelf smartwatches in a real-time drowsiness detection system

Real time Driver’s Drowsiness Detection by Convolution Neural Network (CNN) of Deep Learning Approach

Statistics have shown that 20% of all road accidents are fatigue-related, and drowsy detection is a car safety algorithm that can alert a snoozing driver in hopes of preventing an accident. This work will propose real-time drowsiness detection; this approach is based on Convolution Neural Network (CNN) of Deep Learning. Which is aimed to implement driver’s behavior-based drowsiness detection scenario. Convolution Neural Network (CNN) for learning effective features or facial landmark input to detecting drowsiness by given an input video of driver. A common global face which is not capable enough to extracting effective facial landmarks and features, like facial movements and head gestures, which are strictly important for learning. This proposed work consists Convolution Neural Network (CNN) for attaining well-aligned facial movements and head gestures important for reliable detection. The output of neural network is integrated and feed to classifier for drowsiness detection.

A Cloud-Based Model for Driver Drowsiness Detection and Prediction Based on Facial Expressions and Activities

This paper proposes an efficient approach to detecting and predicting drivers' drowsiness based on the cloud. This work focuses on the behavioral as well as facial expressions of the driver to detect drowsiness. This paper proposes an efficient approach to predicting drivers' drowsiness based on facial expressions and activities. Four different models with distinct features were experimented upon. Of these, two were VGG and the others were CNN and ResNet. VGG models were used to detect the movement of lips (yawning) and to detect facial behavior. A CNN model was used to capture the details of the eyes. ResNet detects the nodding of the driver. The proposed approach also exceeds the results set by the benchmark mode and provides high accuracy, an easy-to-use framework for embedded devices in real-time drowsiness detection. To train the proposed model, the authors have used the National Tsing Hua University (NTHU) Drivers Drowsiness data set. The overall accuracy of the proposed approach is 90.1%.

Eye Aspect Ratio for Real-Time Drowsiness Detection to Improve Driver Safety

Drowsiness is a major risk factor for road safety, contributing to serious injury, death, and economic loss on the road. Driving performance decreases because of increased drowsiness. In several different applications, such as facial movement analysis and driver safety, blink detection is an essential requirement that is used. The extremely rapid blink rate, on the other hand, makes automatic blink detection an extremely challenging task. This research paper presents a technique for identifying eye blinks in a video series recorded by a car dashboard camera in real time. The suggested technique determines the facial landmark positions for each video frame and then extracts the vertical distance between the eyelids from the facial landmark positions. The algorithm that has been proposed estimates the facial landmark positions, extracts a single scalar quantity by making use of Eye Aspect Ratio (EAR), and identifies the eye closeness in each frame. In the end, blinks are recognized by employing the modified EAR threshold value in conjunction with a pattern of EAR values in a relatively short period of time. Experimental evidence indicates that the greater the EAR threshold, the worse the AUC’s accuracy and performance. Further, 0.18 was determined to be the optimum EAR threshold in our research.

Remote Drowsiness Detection Based on the mmWave FMCW Radar

Drowsiness can lead to inefficiency and major disasters; thus it is important to address it in both academia and industry. Despite multiple types of research in this field, a nonintrusive classifier system for detecting drowsiness in real-time under a natural environment without specific stimulation is lacking. This study develops a real-time drowsiness detection system using a 77 GHz millimeter-wave (mmWave) frequency-modulated continuous wave radar. Specifically, firstly, a real-time mmWave processing module is proposed, which can adaptively suppress both stationary and non-stationary clutters. Secondly, a feature extraction module based on a hybrid of handcrafted and machine learning (ML) features is propsoed to obtain a holistic view of mmWave-based vital signals, in which ML features represent linear and temporal changes. Thirdly, a drowsiness classification model is proposed based on feature fusion and extreme gradient boosting algorithms, thus classifying a user’s state into two categories: non-drowsy and drowsy. To confirm the proposed system’s performance, it is validated on a self-collected dataset (n = 28). The experimental results show the following (i) the accuracy of heart rate evaluation is 96.4%, and (ii) based on 10-fold cross-validation, the proposed system gains a detection accuracy of 82.9% and outperforms the state-of-the-art approaches.

Deteksi Kantuk pada Pengemudi Berdasarkan Penginderaan Wajah Menggunakan PCA dan SVM

Drowsiness while driving is one of the main causes of traffic accidents it affects the level of focus of the driver. Therefore, we need an automatic drowsiness detection mechanism for the driver to provide a warning or alarm so that an accident can be avoided. In this study, we design and simulate a system to detect drowsiness through the driver’s yawn expression. The acquisition is made by recording the face from two shooting points including the dashboard and front mirrors in the car. From the video recording, then it is taken into several images with a size of 128x82 pixels which are used as training and testing data. This image is then processed using Principal Component Analysis (PCA) for feature extraction and classified using a Support Vector Machine (SVM). From the tests carried out, the system generates the highest accuracy of 98%. This best performance is obtained by SVM with polynomial kernel in the camera position on the dashboard. Meanwhile, based on compression testing, the image that can still meet system requirements is 25% of the original size. It is hoped that the proposed drowsiness detection method in this study can be applied for real-time drowsiness detection in vehicles.

Driver Drowsiness Detection System using Machine Learning

We propose a Driver drowsiness detection system, the purposes of which are to prevent from dangerous cause and to avoid accidents. Since all the processes on image recognition performed on a smart phone, the system does not need to send images to a server and runs on an android smart phone in a real-time way. Automatic image-based recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches without human supervision real-time drowsiness detection. This model classifies whether the person’s eyes are opened or closed. To recognize the face, a user should have to adjust camera such a way that it covers his face first, and then the system starts recognition within the indicated bounding boxes. In addition, the system estimates the actions of the person. This recognition process is performed repeatedly about every second. We will implement this system as Web application effectively for real-time recognition.

Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection : SEEGDD.

Drowsiness detection is essential in some critical tasks such as vehicle driving, crane operating, mining blasting, and so on, which can help minimize the risks of inattentiveness. Electroencephalography (EEG) based drowsiness detection methods have been shown to be effective. However, due to the non-stationary nature of EEG signals, techniques such as signal transformation and sub-band extraction are increasingly being used to automatically classify awake and drowsy states. Most of these procedures require high computation time. In this paper, analytical and single-feature computation are used to propose a single-channel EEG-based drowsiness detection method to overcome this. Physionet sleep dataset and the simulated virtual driving dataset were used to test the proposed model. When compared to existing work, the proposed approach yields better results.

Driver drowsiness detection system using hybrid approach of convolutional neural network and bidirectional long short term memory (CNN_BILSTM)

Abstract In today’s world driver drowsiness is a major reason for fatal accidents of on road vehicles. Developing an automated, real-time drowsiness detection system is essential to provide accurate and timely alerts to the driver. In the proposed system, hybrid approach of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long Term Dependencies) is used to detect the driver’s drowsiness. Video camera is used to track the facial image and eye blinks of the driver. The proposed system works in three main phases: In the First phase, driver's face image is Identified and observed using a web camera. In the Second phase, the eye image features are extracted using the Euclidean algorithm. During the third phase, the eye blinks are continually monitored. The final stage decides whether the measure in eye square is closed state or open state. When a driver falls asleep, there will be a warning message to alert the driver to prevent road accidents.

Real-Time Drowsiness Detection System for Driver Monitoring

Nowadays, the rate of road accidents due to microsleep has been alarming. During microsleep, people might doze off without realizing it. For many decades, drowsiness detection system for vehicles was not among the major concerns though it turns out as one of imperative features that could have avoid microsleep and thus should be implemented in all vehicles in order to ensure safety of drivers and other vehicles on the road. To the best of our knowledge, enforcements on driving restriction during drowsiness state is yet to be implemented. The absence of such system in the current transportation systems expose drivers to great danger especially at night because accidents are highly likely to happen at night due to drowsy and fatigue drivers. Therefore, this project proposes a real-time drowsiness detection system for vehicles, featuring ignition lock to reduce accidents. An eye blink sensor is embedded in a wearable glasses and heart beat sensor is used to detect drowsiness level of drivers. The system also includes SMS notification system to relatives or friends with location details of the drowsy driver. This project is able to detect and react based on 3 levels of drowsiness by alerting the driver through buzzer. Ignition lock will be applied when high level of drowsiness is detected. Consequently, the vehicle will be slowed down and eventually stopped when dangerous level of drowsiness is detected as a safety precaution.

Single-Channel Real-Time Drowsiness Detection Based on Electroencephalography.

The need of a reliable drowsiness detection system is arising today, as drowsiness is considered as a major cause for accidents as much as alcohol. In this paper, we propose a real-time drowsiness detection algorithm based on a single-channel electroencephalography (EEG) for wearable devices without demanding computing and power resources. The proposed algorithm adopts a cumulative counter to extract important features from 8 different frequency bands: delta (1-3 Hz), theta ($\not\subset-7$ Hz), low-alpha (8-9 Hz), high-alpha (10-12 Hz), low-beta (13-17 Hz), high-beta (18-30 Hz), low-gamma (31-40 Hz), and high-gamma (41-50 Hz). These features are then processed by a support vector machine (SVM) to distinguish between drowsy and awake states. Our preliminary results demonstrate that the proposed algorithm is capable of detecting drowsiness with superior accuracy (83.36%) over the conventional method (70.62%).

Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques

Road crashes and related forms of accidents are a common cause of injury and death among the human population. According to 2015 data from the World Health Organization, road traffic injuries resulted in approximately 1.25 million deaths worldwide, i.e. approximately every 25 seconds an individual will experience a fatal crash. While the cost of traffic accidents in Europe is estimated at around 160 billion Euros, driver drowsiness accounts for approximately 100,000 accidents per year in the United States alone as reported by The American National Highway Traffic Safety Administration (NHTSA). In this paper, a novel approach towards real-time drowsiness detection is proposed. This approach is based on a deep learning method that can be implemented on Android applications with high accuracy. The main contribution of this work is the compression of heavy baseline model to a lightweight model. Moreover, minimal network structure is designed based on facial landmark key point detection to recognize whether the driver is drowsy. The proposed model is able to achieve an accuracy of more than 80%.

Prediction of drowsiness events in night shift workers during morning driving

The morning commute home is an especially vulnerable time for workers engaged in night shift work due to the heightened risk of experiencing drowsy driving. One strategy to manage this risk is to monitor the driver’s state in real time using an in vehicle monitoring system and to alert drivers when they are becoming sleepy. The primary objective of this study is to build and evaluate predictive models for drowsiness events occurring in morning drives using a variety of physiological and performance data gathered under a real driving scenario. We used data collected from 16 night shift workers who drove an instrumented vehicle for approximately two hours on a test track on two occasions: after a night shift and after a night of rest. Drowsiness was defined by two outcome events: performance degradation (Lane-Crossing models) and electroencephalogram (EEG) characterized sleep episodes (Microsleep Models). For each outcome, we assessed the accuracy of sets of predictors, including or not including a driver factor, eyelid measures, and driving performance measures. We also compared the predictions using different time intervals relative to the events (e.g., 1-min prior to the event through 10-min prior). By examining the Area Under the receiver operating characteristic Curve (AUC), accuracy, sensitivity, and specificity of the predictive models, the results showed that the inclusion of an individual driver factor improved AUC and prediction accuracy for both outcomes. Eyelid measures improved the prediction for the Lane-Crossing models, but not for Microsleep models. Prediction performance was not changed by adding driving performance predictors or by increasing the time to the event for either outcome. The best models for both measures of drowsiness were those considering driver individual differences and eyelid measures, suggesting that these indicators should be strongly considered when predicting drowsiness events. The results of this paper can benefit the development of real-time drowsiness detection and help to manage drowsiness to avoid related motor-vehicle crashes and loss.

DEVELOPMENT OF A REAL-TIME SINGLE CHANNEL BRAIN–COMPUTER INTERFACE SYSTEM FOR DETECTION OF DROWSINESS

Drowsiness or fatigue condition refers to feeling abnormally sleepy at an inappropriate time, especially during day time. It reduces the level of concentration and slowdown the response time, which eventually increases the error rate while doing any day-to-day activity. It can be dangerous for some people who require higher concentration level while doing their work. Study shows that 25–30% of road accidents occur due to drowsy driving. There are number of methods available for the detection of drowsiness out of which most of the methods provide an indirect measurement of drowsiness whereas electroencephalography provides the most reliable and direct measurement of the level of consciousness of the subject. The aim of this paper is to design and develop a portable and low cost brain–computer interface system for detection of drowsiness. In this study, we are using three dry electrodes out of which two active electrodes are placed on the forehead whereas the reference electrode is placed on the earlobe to acquire electroencephalogram (EEG) signal. Previous research shows that, there is a measurable change in the amplitude of theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave between the active state and the drowsy state and based on this fact theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave are separated from the normal EEG signal. The signal processing unit is interfaced with the microcontroller unit which is programmed to analyze the drowsiness based on the change in the amplitude of theta ([Formula: see text]) wave. An alarm will be activated once drowsiness is detected. The experiment was conducted on 20 subjects and EEG data were recorded to develop our drowsiness detection system. Experimental results have proved that our system has achieved real-time drowsiness detection with an accuracy of approximately 85%.

Real‐time drowsiness detection using wearable, lightweight brain sensing headbands

The feasibility of real-time drowsiness detection using commercially available, off-the-shelf, lightweight, wearable electroencephalogram (EEG) sensors is explored. While EEG signals are known to be reliable indicators of fatigue and drowsiness, they have not been used widely due to their size and form factor. However, the use of lightweight wearable EEGs alleviates this concern. Spectral analysis of EEG signals from these sensors using support vector machines (SVMs) is shown to classify drowsy states with high accuracy. The system is validated using data collected on 23 subjects in fresh and drowsy states. An accuracy of 81% is obtained at a per-subject level and 74% in cross-subject validation using SVM with radial basis kernel. Using a temporal aggregation strategy, the cross-subject validation accuracy is shown to improve to 87%. The EEG signals are also used to characterise the blink duration and frequency of subjects. However, classification of drowsy states using blink analysis is shown to have lower accuracy than that using spectral analysis.

Real Time Drowsiness Detection System using Viola Jones Algorithm

Among the many problems faced by the country, deaths and injuries due to road traffic accidents are extremely common. Driver’s drowsiness is one of the major reasons which lead to these mishaps. In this paper, we are discussing a ‘Real Time Drowsiness Detection System’ which could determine the level of drowsiness of the driver. This system considers both the closing of eyes and yawning as the constraints for determining the degree of drowsiness. Viola Jones Algorithm is used for facial features detection. Primary attention is given to faster detection and processing of data. Driver’s drowsiness will be detected by checking whether the eyes are closed over some particular consecutive frames and similarly it can be checked if the driver is yawning by monitoring the mouth region. If the closing of eyes and yawning indicates that the driver is drowsy then an alarm will be triggered. General Terms Feature Detection, Computer Vision, Machine Learning

Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection

This paper presents a non-intrusive approach for monitoring driver drowsiness using the fusion of several optimized indicators based on driver physical and driving performance measures, obtained from ADAS (Advanced Driver Assistant Systems) in simulated conditions. The paper is focused on real-time drowsiness detection technology rather than on long-term sleep/awake regulation prediction technology. We have developed our own vision system in order to obtain robust and optimized driver indicators able to be used in simulators and future real environments. These indicators are principally based on driver physical and driving performance skills. The fusion of several indicators, proposed in the literature, is evaluated using a neural network and a stochastic optimization method to obtain the best combination. We propose a new method for ground-truth generation based on a supervised Karolinska Sleepiness Scale (KSS). An extensive evaluation of indicators, derived from trials over a third generation simulator with several test subjects during different driving sessions, was performed. The main conclusions about the performance of single indicators and the best combinations of them are included, as well as the future works derived from this study.