A Framework for Driver DrowsinessMonitoring Using a Convolutional Neural Network and the Internet of Things

One of the major causes of road accidents is sleepy drivers.Such accidents typically result in fatalities and financial losses and disadvantage other road users.Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system.Most studies have examined how the mouth and eyelids move.However,this limits the system’s ability to identify drowsiness traits.Therefore,this study designed an Accident Detection Framework(RPK)that could be used to reduce road accidents due to sleepiness and detect the location of accidents.The drowsiness detectionmodel used three facial parameters:Yawning,closed eyes(blinking),and an upright head position.This model used a Convolutional Neural Network(CNN)consisting of two phases.The initial phase involves video processing and facial landmark coordinate detection.The second phase involves developing the extraction of frame-based features using normalization methods.All these phases used OpenCV and TensorFlow.The dataset contained 5017 images with 874 open eyes images,850 closed eyes images,723 open-mouth images,725 closed-mouth images,761 sleepy-head images,and 1084 non-sleepy head images.The dataset of 5017 images was divided into the training set with 4505 images and the testing set with 512 images,with a ratio of 90:10.The results showed that the RPK design could detect sleepiness by using deep learning techniques with high accuracy on all three parameters;namely 98%for eye blinking,96%for mouth yawning,and 97%for head movement.Overall,the test results have provided an overview of how the developed RPK prototype can accurately identify drowsy drivers.These findings will have a significant impact on the improvement of road users’safety and mobility.

Excessive Drowsiness among Truck Drivers in Benin in 2023: Associated Factors and Risk of Crashes Occurrence

Introduction: Over-drowsiness is a condition with serious consequences, including road accidents. The condition, however, is often ignored both by carers as well as victims themselves. The aim of the present study was to investigate the factors associated with excessive drowsiness in Cotonou, Benin 2023, along with its influence on the occurrence of crashes among truck drivers. Methods: This was a descriptive and analytical cross-sectional study, held from March 13 to April 10, 2023, focusing on large truck drivers over 18 years of age, selected by convenience from parking lots in and around the city of Cotonou. Data collected using questionnaires on socio-demographic and behavioral factors, sleeping habits and working conditions were processed using Stata 15.0 software. Excessive drowsiness was defined by a score above 10 on the Epworth scale. Associated factors were found by multiple logistic regression, at a threshold of 0.05. Results: Altogether 304 drivers, all male and aged 35.98 ± 8.42 years, were surveyed. The prevalence of excessive drowsiness was 29.2%. The associated factors identified were not practicing sport OR = 2.27, CI95% = [1.33;3.86], p = 0,002;sleep duration per working day OR = 1.82;CI95% = [1.06;3.11], p = 0,027 and average distance travelled per day OR = 3.40;CI95% = [1.53;7.56], p = 0,003. Excessive drowsiness was associated with a 1.73-fold increased risk of road accidents (CI95% [1.04;2.87];p = 0.03). Conclusion: Communicating excessive drowsiness and its associated factors to all the stakeholders in the haulage chain is essential to help prevent road accidents.

A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

Today,fatalities,physical injuries,and significant economic losses occur due to car accidents.Among the leading causes of car accidents is drowsiness behind the wheel,which can affect any driver.Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents.This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos.This model depends on integrating a 3D convolutional neural network(3D-CNN)and long short-term memory(LSTM).The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames.The learned features are then used as the input of the LSTM component for modeling high-level temporal features.In addition,we investigate how the training of the proposed model can be affected by changing the position of the batch normalization(BN)layers in the 3D-CNN units.The BN layer is examined in two different placement settings:before the non-linear activation function and after the non-linear activation function.The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD.3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers.We show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the other.As a result,the model achieves a test detection accuracy of 96%,93%,and 90%on YawDD,Side-3MDAD,and Front-3MDAD,respectively.

Real-Time CNN-Based Driver Distraction&Drowsiness Detection System

Nowadays days,the chief grounds of automobile accidents are driver fatigue and distractions.With the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing accidents.This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle.Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is achieved.One of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement agencies.The suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.

基于统计信息的Cache漏流功耗估算方法

随着工艺尺寸的缩小,漏流功耗逐渐成为制约微处理器设计的主要因素之一.Sleep Cache与Drowsy Cache是两种降低Cache漏流功耗的重要技术.基于统计信息的Cache漏流功耗估算方法(SB-CLPE)用于对Sleep Cache或Drowsy Cache进行Cache漏流功耗估算,根据该方法设计的Cache体系结构能够在程序执行过程中实时估算Cache漏流功耗.通过对所有Cache块的访问间隔时间进行统计,SB-CLPE可以估算出使用不同衰退间隔时Cache的漏流功耗,从而得到使Cache漏流功耗最低的最佳衰退间隔.实验表明,SB-CLPE对Sleep Cache的漏流功耗的估算结果与HotLeakage漏流功耗模拟器通过模拟获得的结果相比,平均偏差仅为3.16%,得到的最佳衰退间隔也可以较好吻合.使用SB-CLPE的Cache体系结构可以用于在程序执行过程中对最佳衰退间隔进行实时估算,通过动态调整衰退间隔以达到最优的功耗降低效果.

Multinomial Logistic Regression Model for Predicting Driver＇s Drowsiness Using Only Behavioral Measures

The aim of this study was to explore the effectiveness of behavioral evaluation measures for predicting drivers＇ subjective drowsiness. Behavioral measures included neck bending angle, back pressure, foot pressure, COP （center of pressure） movement on sitting surface and tracking error in driving simulator task. Drowsy states were predicted by means of the multinomial logistic regression model where behavioral measures and subjective evaluation of drowsiness corresponded to independent variables and a dependent variable, respectively. First, we compared the effectiveness of two methods （correlation coefficient-based method and odds ratio-based method） for determining the order of entering behavioral measures into the prediction model. It was found that the prediction accuracy did not differ between both methods. Second, the prediction accuracy was compared among the numbers of behavioral measures. The prediction accuracy did not differ among four, five and six behavioral measures and it was concluded that entering at least four behavioral measures into the prediction model is enough to achieve higher prediction accuracy. Third, the prediction accuracy was compared between the strongly drowsy and the weakly drowsy groups. The prediction accuracy differed between the two groups and the proposed method was effective under the condition where drowsiness was induced to a larger extent.

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

The aim of this study was to predict drivers＇ drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG （Electro encephalography） （EEG-MPF and EEG-α/β）, ECG （Electrocradiogram） （RRV3）, t racking error, an d subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash.

Development and Analysis of a Machine Learning Based Software for Assisting Online Classes during COVID-19

Amid the Covid-19 widespread, it has been challenging for educational institutions to conduct online classes, facing multiples challenges. This paper provides an insight into different approaches in facing those challenges which includes conducting a fair online class for students. It is tough for an instructor to keep track of their students at the same time because it is difficult to screen if any of the understudies within the class are not present, mindful, or drowsing. This paper discusses a possible solution, something new that can offer support to instructors seeing things from a more significant point of view. The solution is a facial analysis computer program that can let instructors know which students are attentive and who is not. There’s a green and red square box for face detection, for which Instructors can watch by seeing a green box on those mindful students conjointly, a red box on those who are not mindful at all. This paper finds that the program can automatically give attendance by analyzing data from face detection. It has other features for which the teacher can also know if any student leaves the class early. In this paper, model design, performance analysis, and online class assistant aspects of the program have been discussed.

Relationship between Lifestyle, Quality of Sleep, and Daytime Drowsiness of Nursing Students of University A

The harmful effects of technological devices, including smart phones have been increasingly suspected among university students;bedtimes have become increasingly later at night, and leisure activities often extend through the night. Likewise, availability and need of increasing part-time job hours have been considered. The purpose of this research was to determine the relationship among lifestyles, quality of sleep, and daytime drowsiness of nursing students of University A. The research was conducted in June 2015, when student life rhythms were considered stable after two months of lectures. Responses with missing values or with inappropriate answers were excluded. Of the data collected from 96 respondents, only 71 were acceptable. The survey focused on lifestyle, daytime sleepiness (using ESS: Epworth Sleepiness Scale) and quality of subjective sleep (using the PSQI: Pittsburgh Sleep Quality Index). Approval was obtained from the Research Ethics Committee of Shikoku University. While in this study, more than half (63.4%) of the students had poor quality of sleep, however, there was no relationship between their quality of sleep and daytime drowsiness, or between their lifestyles and the quality of sleep. These findings suggest that while university students’ use of technological devices is suspected to influence on sleep deprivation and consequent daytime drowsiness, the findings did not provide the evidence.

Real-Time Detection of Human Drowsiness via a Portable Brain-Computer Interface

In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.

A systematic review of the effect of various interventions on reducing fatigue and sleepiness while driving

Purpose： To identify and appraise the published studies assessing interventions accounting for reducing fatigue and sleepiness while driving. Methods： This systematic review searched the following electronic databases： Medline, Science direct, Scopus, EMBASE, PsyclNFO, Transport Database, Cochrane, BIOSIS, ISI Web of Knowledge, specialist road injuries journals and the Australian Transport and Road Index database. Additional searches included websites of relevant organizations, reference lists of included studies, and issues of major injury journals published within the past 15 years. Studies were included if they investigated interventions/exposures accounting for reducing fatigue and sleepiness as the outcome, measured any potential interventions for mitigation of sleepiness and were written in English. Meta-analysis was not attempted because of the heterogeneity of the included studies. Results： Of 63 studies identified, 18 met the inclusion criteria. Based on results of our review, many interventions in the world have been used to reduce drowsiness while driving such as behavioral （talking to passengers, face washing, listening to the radio, no alcohol use, limiting the driving behavior at the time of 12 p.m. - 6 a.m. etc）, educational interventions and also changes in the environment （such as rumble strips, chevrons, variable message signs, etc）. Meta-analysis on the effect of all these in- terventions was impossible due to the high heterogeneity in methodology, effect size and interventions reported in the assessed studies. Conclusion： Results of present review showed various interventions in different parts of the world have been used to decrease drowsy driving. Although these interventions can be used in countries with high incidence of road traffic accidents, precise effect of each intervention is still unknown. Further studies are required for comparison of the efficiency of each intervention and localization of each intervention ac- cording to the traffic pattems of each country.

Driver Drowsiness Recognition Based on Computer Vision Technology

Driver drowsiness is one of the major causes of traffic accidents. This paper presents a nonintru- sive drowsiness recognition method using eye-tracking and image processing. A robust eye detection algo- rithm is introduced to address the problems caused by changes in illumination and driver posture. Six measures are calculated with percentage of eyelid closure, maximum closure duration, blink frequency, av- erage opening level of the eyes, opening velocity of the eyes, and closing velocity of the eyes. These meas- ures are combined using Fisher＇s linear discriminant functions using a stepwise method to reduce the cor- relations and extract an independent index. Results with six participants in driving simulator experiments demonstrate the feasibility of this video-based drowsiness recognition method that provided 86% accuracy.

Effect of acupuncture on the daytime function in patients with post-stroke sleep-wake disturbance:A randomized controlled trial

Objective:To explore the improvements in sleep duration,drowsiness and daytime fatigue in patients with sleep-wake disturbance after ischemic stroke treated by needling at Baihui(百会GV20)and Sishencong(四神聪EX-HN1).Methods:A total of 81 patients with post-stroke sleep-wake disturbance were randomly divided into 2 groups,i.e.a control group(39 cases)and a treatment group(42 cases).In the control group,the basic treatment for ischemic stroke was administered.In the treatment group,on the base of the treatment as the control group,acupuncture was applied to GV20 and EX-HN1.The treatment was given once a day,30 min each time,totally for 4 weeks.The daytime sleep duration,sleep need,Epworth Sleepiness Score(ESS)and Fatigue Severity Scale(FSS)score were observed in the patients of two groups before and after treatment.The therapeutic effects were observed.Results:After treatment,the daytime sleep duration and sleep need were all reduced to different extents in the patients of two groups.The daytime sleep duration and sleep need in the treatment group were lower than those in the control group(all P<0.05).After treatment,ESS of two groups were reduced significantly to be(11.21±3.33)points and(6.40±2.11)points,respectively(both P<0.05)and the FSS scores reduced significantly to be(33.62±9.17)points and(21.45±8.17)points,respectively(both P<0.05).Conclusion:Acupuncture at GV20 and EX-HN1 effectively reduces daytime sleep duration,relieves drowsiness and daytime fatigue and improves daytime function of the patients with post-stroke sleepwake disturbance.

An ensemble learning model for driver drowsiness detection and accident prevention using the behavioral features analysis

Purpose-In order to prevent accidents during driving,driver drowsiness detection systems have become a hot topic for researchers.There are various types of features that can be used to detect drowsiness.Detection can be done by utilizing behavioral data,physiological measurements and vehicle-based data.The existing deep convolutional neural network(CNN)models-based ensemble approach analyzed the behavioral data comprises eye or face or head movement captured by using a camera images or videos.However,the developed model suffered from the limitation of high computational cost because of the application of approximately 140million parameters.Design/methodology/approach-The proposed model uses significant feature parameters from the feature extraction process such as ReliefF,Infinite,Correlation,Term Variance are used for feature selection.The features that are selected are undergone for classification using ensemble classifier.Findings-The output of these models is classified into non-drowsiness or drowsiness categories.Research limitations/implications-In this research work higher end camera are required to collect videos as it is cost-effective.Therefore,researches are encouraged to use the existing datasets.Practical implications-This paper overcomes the earlier approach.The developed model used complex deep learning models on small dataset which would also extract additional features,thereby provided a more satisfying result.Originality/value-Drowsiness can be detected at the earliest using ensemble model which restricts the number of accidents.