Drowsy State Research Articles

Alat Pencegah Dini Kecelakaan Pada Kendaraan Bermotor Berbasis Internet of Things (Studi Kasus Jalur Sitinjau Lauik)

The creation of an Internet of Things-based motor vehicle early prevetion tool (Sitinjau Lauik Case Study) has been completed.. The aim of this research is to develop a tool to minimize accidents caused by human physiological factors such as fatigue and drowsiness. This device utilizes the MPU 6050 sensor, pulse heart sensor, buzzer, vibration motor, GPS, Arduino Uno and Wemos D1 Mini. The research method began with the construction of the device and the permance testing of the system. The results indicate that the system of this device can detect drowsiness or fatigue by measuring the heart rate at less than 70 bpm using the pulse heart sensor and detecting head movements using the MPU 6050 sensor, where the X- axis angle is > 29,23 ° < - 30,63 ° and the Y-axis angle is > 29,33° < -20,12°. When drowsiness is detected, the device sends notifications via the Telegram application containing the driver’s condition, heart rate, andlocation through a Google Maps link. Subsequently, when the driver is drowsy, the buzzer sounds to provide a warning and the vibration motor provides vibration to give a sensory wake-up signal to the driver, helping to awaken them from a drowsy state. Overall, the device functions effectively.

Detection of driver drowsiness level using a hybrid learning model based on ECG signals.

Fatigue has a considerable impact on the driver's vehicle and even the driver's own operating ability. An intelligent algorithm is proposed for the problem that it is difficult to classify the degree of drowsiness generated by the driver during the driving process. By studying the driver's electrocardiogram (ECG) during driving, two models were established to jointly classify the ECG signals as awake, stress, and fatigue or drowsiness states for drowsiness levels. Firstly, the deep learning method was used to establish the model_1 to predict the drowsiness of the original ECG, and model_2 was developed using the combination of principal component analysis (PCA) and weighted K-nearest neighbor (WKNN) algorithm to classify the heart rate variability characteristics. Then, the drowsiness prediction results of the two models were weighted according to certain rules, and the hybrid learning model combining dilated convolution and bidirectional long short-term memory network with PCA and WKNN algorithm was established, and the mixed model was denoted as DiCNN-BiLSTM and PCA-WKNN (DBPW). Finally, the validity of the DBPW model was verified by simulation of the public database. The experimental results show that the average accuracy, sensitivity and F1 score of the test model in the dataset containing multiple drivers are 98.79, 98.81, and 98.79 % respectively, and the recognition accuracy for drowsiness or drowsiness state is 99.33 %. Using the proposed algorithm, it is possible to identify driver anomalies and provide new ideas for the development of intelligent vehicles.

Drowsiness Detection using Galvanic Skin Response and Electro-occulograph

Galvanic Skin Response (GSR) is widely used in psychological applications, mostly stress detection. Hence, people always put a limitation on GSR for stress detection only. Therefore, the challenge in this study is to expand the usage of GSR in drowsiness detection. Workers, students and drivers face a sleep deprivation problem due to never-ending work. Hence, this drowsiness detection is needed to detect the drowsiness to prevent unforeseen accidents from occurring. However, existing GSR application on sleep deprivation detection needs to improve with data reliability since the recording always took place on the wrist, and an external source like hand movements may influence the reading. Therefore, another drowsiness detection method is needed for reliable data or tasks. Hence, this study aims to detect GSR and EOG from behind the ear. The earpiece has been designed to make data recording of both GSR and EOG easier. By doing so, this study able to detect the skin conductance response (SCR) and skin resistance level (SCL) of GSR also eye activity which reflect the drowsiness seen from behind the ear of the user. The study found that the SCR and SCL levels increase with increasing sleepiness or drowsiness. Moreover, EOG shows a sudden spike in the signal when the user is in a drowsy state.

Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography Signals.

Technological advancements in healthcare, production, automobile, and aviation industries have shifted working styles from manual to automatic. This automation requires smart, intellectual, and safe machinery to develop an accurate and efficient brain-computer interface (BCI) system. However, developing such BCI systems requires effective processing and analysis of human physiology. Electroencephalography (EEG) is one such technique that provides a low-cost, portable, non-invasive, and safe solution for BCI systems. However, the non-stationary and nonlinear nature of EEG signals makes it difficult for experts to perform accurate subjective analyses. Hence, there is an urgent need for the development of automatic mental state detection. This paper presents the classification of three mental states using an ensemble of the tunable Q wavelet transform, the multilevel discrete wavelet transform, and the flexible analytic wavelet transform. Various features are extracted from the subbands of EEG signals during focused, unfocused, and drowsy states. Separate and fused features from ensemble decomposition are classified using an optimized ensemble classifier. Our analysis shows that the fusion of features results in a dimensionality reduction. The proposed model obtained the highest accuracies of 92.45% and 97.8% with ten-fold cross-validation and the iterative majority voting technique. The proposed method is suitable for real-time mental state detection to improve BCI systems.

A regression model combined convolutional neural network and recurrent neural network for electroencephalogram-based cross-subject fatigue detection.

Fatigue, one of the most important factors affecting road safety, has attracted many researchers' attention. Most existing fatigue detection methods are based on feature engineering and classification models. The feature engineering is greatly influenced by researchers' domain knowledge, which will lead to a poor performance in fatigue detection, especially in cross-subject experiment design. In addition, fatigue detection is often simplified as a classification problem of several discrete states. Models based on deep learning can realize automatic feature extraction without the limitation of researcher's domain knowledge. Therefore, this paper proposes a regression model combined convolutional neural network and recurrent neural network for electroencephalogram-based (EEG-based) cross-subject fatigue detection. At the same time, a twofold random-offset zero-overlapping sampling method is proposed to train a bigger model and reduce overfitting. Compared with existing results, the proposed method achieves a much better result of 0.94 correlation coefficient (COR) and 0.09 root mean square error (RMSE) in a within-subject experiment design. What is more, there is no misclassification between awake and drowsy states. For cross-subject experiment design, the COR and RMSE are 0.79 and 0.15, respectively, which are close to the existing within-subject results and better than similar cross-subject results. The cross-subject regression model is very important for fatigue detection application since the fatigue indication is more precise than several discrete states and no model calibration is required for a new user. The twofold random-offset zero-overlapping sampling method can also be used as a reference by other EEG-based deep learning research.

IoT based Driver Drowsiness and Pothole Detection Alert System

One of the common in progressing countries is the maintenance of roads. Well maintained roads contribute a major portion to the country’s economy. Identification of pavement distress such as potholes and humps not only help drivers to avoid accidents or vehicle damages, but also helps authorities to maintain roads. This paper discusses various pothole detection methods that have been developed and proposes a simple and cost-effective solution to identify the potholes and humps on roads and provide timely alerts to drivers to avoid accidents or vehicle damages. Not only Potholes and humps are the main cause of accidents other than over speeding and drowsiness of driver includes the issue of accidents. Drowsy state may be caused by lack of sleep, medication, tiredness, drugs or driving continuously for long period of time. So, here is the solution for detecting the potholes and humps and to alert the driver from drowsiness while driving. In this paper, the system is structured to detect potholes and to alert the drowsy driver by using the ultrasonic sensor, eyeblink sensor and IR sensor and microcontroller. Ultrasonic sensor senses the humps, IR sensor senses the potholes and eye blink sensor the blinking of eye and this sensing signals fed into the Arduino to alert the driver by buzzer sound.

Algorithms Comparison in Drowsiness Detection

Drowsy driving is a recognized leading cause of road accidents, resulting in a considerable number of fatalities and injuries. This paper presents a proposed system that leverages machine learning algorithms, specifically Convolutional Neural Network (CNN) and Support Vector Machine (SVM), to accurately detect the drowsy state of drivers by analyzing the diameter of their eyes and comparing the level of dilation or constriction. Drowsy driving poses a significant problem on roadways, as indicated by the National Highway Traffic Safety Administration's data, which reports approximately 100,000 police-reported collisions each year involving drowsy driving, leading to over 1,550 fatalities and 71,000 injuries. The proposed system demonstrates the potential to reduce accidents associated with drowsy driving. We conducted an evaluation and comparison of the effectiveness of CNN and SVM algorithms with the objective of identifying the optimal algorithm for drowsiness detection. Our algorithms were trained on a comprehensive dataset comprising images of drowsy and alert drivers, enabling real-time and accurate identification of the driver's state. Employing advanced image processing techniques, the proposed system analyzes changes in eye diameter associated with drowsiness. Its purpose is to promptly alert drivers, thus mitigating accidents caused by drowsy driving. We anticipate that this system will provide a reliable and cost-effective solution to the problem of drowsy driving, with potential benefits for both drivers and passengers. Further research and development efforts could facilitate its widespread adoption in the automotive industry. This paper underscores the significance of addressing drowsy driving and introduces a promising solution through the application of machine learning algorithms.

Multivariate Multiscale Entropy: An Approach to Estimating Vigilance of Driver

Various driver’s vigilance estimation techniques currently exist in the literature. But none of them estimates the driver’s vigilance in the complexity domain. In this research, we propose the recently introduced multivariate multiscale entropy method to fill the above mentioned research gap. We apply this technique to differential entropy features of electroencephalogram and electrooculogram signals to detect driver’s vigilance. Also, we employ it to the percentage of eye closure values to analyse the driver’s cognitive states (awake, tired and drowsy) in the complexity domain. The contribution of this research is to efficiently classify the driver’s cognitive states using a new feature based on multivariate multiscale entropy. The experimental complexity profile curves show the statistically significant differences (p < 0.01) among brain electroencephalogram, forehead electroencephalogram and electrooculogram signals. Moreover, the difference in the multivariate sample entropy across all scales in awake (1.0828 ± 0.4664), tired (0.7841 ± 0.3183) and drowsy (0.2938 ± 0.1664) states are statistically significant (p <0.01). Also, the support vector machine, a machine learning technique, discriminates the driver’s cognitive states with a promising classification accuracy of 76.2%. Therefore, the complexity profile of driver’s cognitive states could be an indicator for vigilance estimation.

Using Facial Features to Detect Driver’s Drowsiness State

Abstract: Drowsiness and fatigue are one of the main causes leading to road accidents. They can be prevented by taking effort to get enough sleep before driving, drink coffee or energy drink, or have a rest when the signs of drowsiness occur. The popular drowsiness detection method uses complex methods, such as EEG and ECG [19]. This method has high accuracy for its measurement but it need to use contact measurement and it has many limitations on driver fatigue and drowsiness monitor. Thus, it is not comfortable to be used in real time driving. This paper proposes a way to detect the drowsiness signs among drivers by measuring the eye closing rate and yawning. We provide a robust and intelligent strategy for detecting driver tiredness in this work to address this growing problem. This method involves installing a camera inside the car to record the driver's facial look. The first phase involves using computer vision algorithms to identify and track the face region in the recorded video sequence. After that, the head is removed and its lateral and frontal assent are examined for indications of driver weariness. The driver's state is finally assessed during the fusion phase, and if drowsiness is found, a warning message and an alert are given to the driver. Our tests provide strong support for the proposed theory. The parameters of the eyes and mouth detection are created within the face image. The video was change into images frames per second. From there, locating the eyes and mouth can be performed. Once the eyes are located, measuring the intensity changes in the eye area determine the eyes are open or closed. The major goal of this research is to create a non-intrusive system that can recognise driver weariness and deliver a prompt warning.

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

The number of vehicles on the road rises in tandem with the development of vehicle manufacture. With the rapid growth of automobiles, the number of road accidents appears to be steadily increasing. Accidents are a typical occurrence in our daily lives. Road accidents are the top cause of death from injuries worldwide in the top 10. It now forms an amazingly important part of the global burden of illness. An estimated 1.2 million people are killed in accidents every year, and an estimated 50 million are injured while receiving bone marrow transplants in leading countries. Drowsiness, along with fatigue of drivers, are some of the major causes of road accidents. Every year, we see an increase in death numbers worldwide. This study also looks into automatically detecting driver drowsiness with AI, visuals, and CNN. The Driver Drowsiness System works on the concept of Multi-Layer Feed Forward Network, especially on Convolutional Neural Networks (CNN). CNN was developed with the help of around 7000 images of eyes in both drowsiness and non-drowsiness states with different facial architectures. These images were split into training (80 percent of images) and testing (20 percent of images) datasets. The images under the training dataset are given as the input for the network for training purposes. Backpropagation algorithms and optimizers are used to reduce the loss as much as possible. We created an algorithm for detecting, tracking, and analysing motor and visual effects to measure ROI.

Real-Time Drowsiness Identification Based On Eye State Analysis

As per the previous year’s report concerning to road crashes indicates that the principal cause of such a fatal road accidents is because of negligence behavior as well as drowsiness of driver. This problem reveals the requirement of such a system that can recognize drowsiness state of driver and gives alert signal to the driver before the occurrence of any accidents. Therefore, this proposed work has established drowsy detection as well as accident avoidance system based on the eye blink duration. Here, first the open and close state of eye are detected based on the eye aspect ratio (EAR). Further, the blink duration or count during the changes of eye state from open to close are analyzed. Then, it identifies the state of drowsiness, when blink duration becomes more than a certain limits and sends the alert message to the driver through the alarm. Our developed system has shown the accuracy of 92.5 % approx on yawning dataset (YawDD). Key Word: Eye blink detection; Drowsiness Detection; Eye Aspect Ratio (EAR)

Real Time Prevention of Driver Fatigue Using Deep Learning and MediaPipe

This paper describes the development of a system for detecting driver drowsiness whose goal is to alert drivers of their sleepy state to prevent traffic accidents. It is essential that drowsiness detection in a driving environment be conducted in a non-intrusive manner and that the driver not be troubled by alerts when they are not sleepy. We make use of the MediaPipe Facemesh framework to extract facial features and the Binary Classification Neural Network to precisely detect drowsy states in our solution to this open problem. The solution that minimize false positives is created to determine whether or not the driver exhibits sleepiness symptoms. The approach extracts numerical features from images using deep learning techniques, which are then added to a fuzzy logic-based system. This system typically achieve 91% accuracy on training data and 92% accuracy on test data. The fuzzy logic-based approach, however, stands out because it doesn't raise erroneous alerts (percentage of correctly identified footage where the driver is not tired). Although the findings are not particularly satisfying, the recommendations offered in this study are promising and may be used as a strong platform for future work.

Driver Drowsiness Detection and Alert System based on Visual Analysis using Machine Learning

Abstract: The main idea behind this project is to develop a system that can detect the drowsiness of the driver and issue a timely warning. Driver Fatigue is the main reason for a large number of road accidents. The detection can be done in many different ways and by using different parameters. The proposed system uses the behavior of parameters. Our proposed method can distinguish the simulated drowsy and sleepy states from the normal state of driving on the resolution images of faces and eyes observed from an oblique viewing angle. This system treats the automatic detection of the driver's drowsiness based on visual information and artificial intelligence.

Accident Prevention: Driver’s Drowsiness Detection System Using AI Techniques

One of the main causes of traffic accidents is driver fatigue and drowsiness. Globally, they are increasing the number of fatalities and injuries each year. In this paper, main module focuses on how to recognize tiredness, which will assist to decrease accidents and improve roadsafety. This system gathers photos from a live webcam feed, applies machine learning to the image, and determines whether the driver is sleepy. Many facial expressions and bodily movements, including yawning and sleepy eyes, seen as indicators ofsleepiness. The EAR (Eye Aspect Ratio) measures the ratio of distances between the horizontal and vertical eye landmarks to identify sleepiness. The distance between the lower and upper lips used to produce a YAWN value for yawn detection, and the result is compared to a threshold value. We have used a play sound library, which will giveappropriate voice alert messages when the driver is in a drowsy state. A time limit is applied for driving, if the driver exceeds the time limit then the also system gives the alert message. The proposed system madeto decrease the rate of accidents.

The Clinical Utility of Long-Term Video-EEG Monitoring in Patients Hospitalized for New-Onset Seizures (P6-1.004)

<h3>Objective:</h3> The objective of this study is to evaluate if long-term EEG monitoring (LTM) increases the detection rate of epileptiform abnormalities and seizures as compared to a standard 30-minute EEG in patient’s with first time seizures. <h3>Background:</h3> The usage of LTM has increased in the hospital setting for patients with altered mental status and paroxysmal spells, however, there is limited data regarding the utility of long term EEG monitoring in patients hospitalized for a new-onset seizure. <h3>Design/Methods:</h3> This was a retrospective cohort study including adult patients (&gt;18 years) admitted to two tertiary care hospitals between 1/1/2014 and 12/31/2019. Patients who were given a clinical diagnosis of seizure and were initiated with LTM (monitoring duration of greater than 30 minutes) were included in this study. 295 consecutive patients were initially identified. Patients with a prior diagnosis of seizure/epilepsy or an alternative diagnosis to seizure were excluded. 84 patients qualified for the study. <h3>Results:</h3> 41/84 (49%) patients had EEG recordings of 12–24 hours and 16/84 (19%) had recordings for &gt;24 hours. Drowsy and Sleep states were captured in 82/84 (97.6%). 25/84 (30%) patients were found to have epileptiform activity. Of those, 18/25 (72%) had epileptiform activity occurring within the first 30 minutes. 3/25 (12%) had their first epiletiform activity between 30 and 120 minutes and 4/25 (16%) between 2–12 hours. <h3>Conclusions:</h3> This study raises the possibility of increasing EEG yield with a longer recording period when compared to a 30-minute EEG record. EEG findings that prompted management changes were noted after 30 minutes in just under a third of those found to have epileptiform activity on EEG. This suggests a longer EEG recording period especially in patients with a high clinical suspicion of first-time seizure, may be helpful in determining management. Further prospective studies with a larger sample size, would be of benefit and provide further insight. <b>Disclosure:</b> Dr. Cheng has nothing to disclose. Dr. Rameez has nothing to disclose. Dr. Abbas has nothing to disclose. David E. Burdette, MD has nothing to disclose. Dr. Tank has nothing to disclose. Dr. Chen has nothing to disclose. Rachel Fabris has nothing to disclose. Dr. Okazaki has nothing to disclose. Mohammad Haykal, MD has nothing to disclose.

Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation

Drowsy driving is a common, but underestimated phenomenon in terms of associated risks as it often results in crashes causing fatalities and serious injuries. It is a challenging task to alert or reduce the driver’s drowsy state using non-invasive techniques. In this study, a drowsiness reduction strategy has been developed and analyzed using exposure to different light colors and recording the corresponding electrical and biological brain activities. 31 subjects were examined by dividing them into 2 classes, a control group, and a healthy group. Fourteen EEG and 42 fNIRS channels were used to gather neurological data from two brain regions (prefrontal and visual cortices). Experiments shining 3 different colored lights have been carried out on them at certain times when there is a high probability to get drowsy. The results of this study show that there is a significant increase in HbO of a sleep-deprived participant when he is exposed to blue light. Similarly, the beta band of EEG also showed an increased response. However, the study found that there is no considerable increase in HbO and beta band power in the case of red and green light exposures. In addition to that, values of other physiological signals acquired such as heart rate, eye blinking, and self-reported Karolinska Sleepiness Scale scores validated the findings predicted by the electrical and biological signals. The statistical significance of the signals achieved has been tested using repeated measures ANOVA and t-tests. Correlation scores were also calculated to find the association between the changes in the data signals with the corresponding changes in the alertness level.

Early Detection of Drowsiness While Driving Using Bio Signals

Recently, driver fatigue is considered worldwide as the main reason of vehicle accidents. However, measuring of the driver fatigue can be directly throughout measuring the driver drowsiness state. This paper is aimed to build a drowsiness detection system. The system applied consists of two part: pre-recorded EOG signals and application of IR sensor on the eyes blinking. The results revealed a good detection of the eyes blinking based on a biological signal from the sensors. Wearable driver drowsiness detection system is a suitable alternative for reducing vehicle accidents.

System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures.

The amount of road accidents caused by driver drowsiness is one of the world's major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver's body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver's facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model's efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.

Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network.

Accurate identification of driver's drowsiness state through Electroencephalogram (EEG) signals can effectively reduce traffic accidents, but EEG signals are usually stored in various clients in the form of small samples. This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver's drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver's privacy and improves communication efficiency.

EEG-based drowsiness analysis using OpenBCI and deep learning approaches to establish driving safety

Drowsiness is the inactivated state of the brain and observed during the transition from awaken state to sleepy state. This inactive state diminishes an individual’s attention and leads to accidents during professional or personal activities. The prediction of this inactive (drowsiness) state using AI plays a substantial role in the avoidance of accidents. The advancements in the field of Artificial Intelligence and Neuroscience approaches are used for the prediction of this inactive drowsy state. In order to prevent these devastating accidents, the state of drowsiness of the driver has to be be monitored. Electroencephalogram (EEG) is a predominant tool adopted to examine various states of the brain effectually. It is generally known as Brain-Computer Interface System. The EEG channels are used for predicting the inactive state while implementing the real-time applications. However, the researchers face various challenges during execution based on the classification and channel selection process. This research concentrates on modelling and efficient drowsiness prediction methods and intends to bridge the gap encountered in the existing approaches. A novel stacked Long Short-Term Memory(s - LSTM) with Deep Fully Connected- Convolutional Neural Network (DFC - CNN) is used to learn and memorize the long-term feature dependencies and attains essential information based on time-series prediction. Single and multi-channel EEG data is considered to measure the statistical characteristics of available EEG signals. The online available OpenBCI sleep analysis data is used for performing the experimentation, and run in GoogleColab environment. The proposed s - LSTM model provides a better trade-off compared to existing approaches. The model generalization is improved with the validation of combined feature subjects. Here, metrics like prediction accuracy, RMSE, false positives, scaling coefficients related to false positives are measured to show the significance of the model.