Percentage Of Eyelid Closure Research Articles

Active Vision-Based Attention Monitoring System for Non-Distracted Driving

Inattentive driving is a key reason of road mishaps causing more deaths than speeding or drunk driving. Research efforts have been made to monitor drivers’ attentional states and provide support to drivers. Both invasive and non-invasive methods have been applied to track driver’s attentional states, but most of these methods either use exclusive equipment which are costly or use sensors that cause discomfort. In this paper, a vision-based scheme is proposed for monitoring the attentional states of the drivers. The system comprises four major modules-cue extraction and parameter estimation, state of attention estimation, monitoring and decision making, and level of attention estimation. The system estimates the attentional level and classifies the attentional states based on the percentage of eyelid closure over time (PERCLOS), the frequency of yawning and gaze direction. Various experiments were conducted with human participants to assess the performance of the suggested scheme, which demonstrates the system’s effectiveness with 92% accuracy.

Robust Detection of Fatigue Parameters Based on Infrared Information

Driver fatigue is one of the major causes of traffic accidents, and this need has increased the amount of driver fatigue detection systems in vehicles in order to reduce human and material losses. This work puts forward an approach based on capturing near-infrared videos from a camera mounted inside the vehicle. Then, from the captured images and using image-processing techniques the eyes are detected. Next, features are extracted from eye images using several transforms and finally, the system detects if there is fatigue or not using a SVM as classifier. Throughout the recording, eye position is tracked with a low computational time and fatigue is analysed based on the percentage of eyelid closure. This approach has been developed on two public datasets. Our experiments were able to reach an eye recognition rate of up to 96.87% and our results showed that SVM with RBF kernel were 99.66% accurate on one of the databases used for the system training. This approach shows promising results in comparison with the state of the art and deep learning approaches in order to be implemented in real conditions.

Driver Fatigue Detection Based on Convolutional Neural Networks Using EM-CNN.

With a focus on fatigue driving detection research, a fully automated driver fatigue status detection algorithm using driving images is proposed. In the proposed algorithm, the multitask cascaded convolutional network (MTCNN) architecture is employed in face detection and feature point location, and the region of interest (ROI) is extracted using feature points. A convolutional neural network, named EM-CNN, is proposed to detect the states of the eyes and mouth from the ROI images. The percentage of eyelid closure over the pupil over time (PERCLOS) and mouth opening degree (POM) are two parameters used for fatigue detection. Experimental results demonstrate that the proposed EM-CNN can efficiently detect driver fatigue status using driving images. The proposed algorithm EM-CNN outperforms other CNN-based methods, i.e., AlexNet, VGG-16, GoogLeNet, and ResNet50, showing accuracy and sensitivity rates of 93.623% and 93.643%, respectively.

Real-time assessment of daytime sleepiness in drivers with multiple sclerosis

BackgroundDaytime sleepiness is a common symptom of multiple sclerosis (MS) that may jeopardize safe driving. Our aim was to compare daytime sleepiness, recorded in real-time through eyelid tracking, in a simulated drive between individuals with MS (iwMS) and healthy controls. MethodsFifteen iwMS (age = median (Q1 – Q3), 55 (50 – 55); EDSS = 2.5 (2 – 3.5); 12 (80%) female) were matched for age, sex, education, and cognitive status with 15 controls. Participants completed self-reported fatigue and sleepiness scales including the Modified Fatigue Impact Scale (MFIS), Pittsburg Sleep Quality Inventory (PSQI), and Epworth Sleepiness Scale (ESS). Percentage of eyelid closure (PERCLOS) was extracted from a remote eye tracker while completing a simulated drive of 25 min. ResultsAlthough iwMS reported more symptoms of fatigue (MFIS, p = 0.003) and poorer sleep quality (PSQI, p = 0.008), they did not report more daytime sleepiness (ESS, p = 0.45). Likewise, there were no differences between groups in real-time daytime sleepiness, indexed by PERCLOS (p = 0.82). Both groups exhibited more real-time daytime sleepiness as they progressed through the drive (time effect, p < 0.0001). The interaction effect of group*time (p = 0.05) demonstrated increased symptoms of daytime sleepiness towards the end of the drive in iwMS compared to controls. PERCLOS correlated strongly (Spearman ρ = 0.76, p = 0.001) with distance out of lane in iwMS. ConclusionIwMS show exacerbated symptoms of daytime sleepiness during a monotonous, simulate drive. Future studies should investigate the effect of MS on daytime sleepiness during real-world driving.

Stretchable, transparent triboelectric nanogenerator as a highly sensitive self-powered sensor for driver fatigue and distraction monitoring

Abstract The ever-increasing automobiles have caused large number of traffic accidents every year. Fatigue driving and distracted driving are two main reasons for most of traffic accidents. Thus, intelligent transportation has attracted much attention. In this work, a stretchable polyacrylamide (PAAM) -LiCl-based triboelectric nanogenerator (PL-TENG) is utilized for the first time to monitor driver fatigue and distraction by attaching them to the face and neck of a driver. These PL-TENG sensors can detect eye closure, mouth closure, and neck rotation with high accuracy. Eye blink duration (BD), blink interval duration (BID), percentage of eyelid closure over time (PERCLOS) and yawn frequency (YF) are chosen as indicators of driver fatigue, while mouth closure and head positioning are chosen as indicators of driver distraction. The PL-TENG sensor has the characteristics of high voltage, high sensitivity and good biocompatibility, which demonstrates that the PL-TENG sensors are more sensitive than traditional fatigue detection systems. This work paves a new way for designing highly sensitive self-powered sensor in an intelligent transportation system.

Exercise Fatigue Detection Algorithm Based on Video Image Information Extraction

Excessive psychological pressure, long working hours, and excessive labor intensity can make people exhausted and affect people's cognition and motor function. Detecting the fatigue state of athletes can prevent excessive fatigue and sports injuries. This article chooses the adaptive median filter method to smooth the image and remove the noise, and uses the adaptive threshold light equalization method to adjust the image's light equalization. According to the admission and rejection criteria of the Sequential Forward Floating Selection (SFFS) algorithm, different feature parameter combinations are used to build a fatigue motion detection model based on Support Vector Machine (SVM). Taking the classification performance of the built SVM detection model as the evaluation criterion, and using the sequence floating forward selection algorithm as the search strategy, the fatigue characteristic parameter optimization selection algorithm is established. The algorithm is used to reduce the dimensionality of the full set of fatigue feature parameters, and the optimal feature subset of fatigue motion is extracted. Based on the paired sample t-test and the analysis of variance method, it analyzes and quantifies the comprehensive influence of individual athlete differences and fatigue exercise on sports behavior and eye movement characteristics. An adaptive detection model is built based on personality parameters, and the design idea of the fatigue feature extraction network is analyzed. In order to make full use of the information of the feature vector output by the fully connected layer, the new network designs two fully connected layers to extract feature vectors. Two types are output by the Softmax loss function, which can directly determine whether the athlete is in a fatigue state. Based on the PERCLOS (Percentage of Eyelid Closure Over the Pupil over time) criterion, this article completes the construction of the fatigue motion sample set, and classifies the face images with more than 80% eyes closed as fatigue samples. This method can apply the PERCLOS criterion to the training of the convolutional neural network, so that it can recognize the fatigue state of the face based on the comprehensive facial features and improve the robustness of the algorithm.

A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers

This paper presents a smartphone-based system for the detection of drowsiness in automotive drivers. The proposed framework uses three-stage drowsiness detection. The first stage uses the percentage of eyelid closure (PERCLOS) obtained through images captured by the front camera with a modified eye state classification method. The system uses near infrared lighting for illuminating the face of the driver during night-driving. The second step uses the voiced to the unvoiced ratio obtained from the speech data from the microphone, in the event PERCLOS crosses the threshold. A final verification stage is used as a touch response within a stipulated time to declare the driver as drowsy and subsequently sound an alarm. The device maintains a log file of the periodic events of the metrics along with the corresponding GPS coordinates. The system has three advantages over existing drowsiness detection systems. First, the three-stage verification process makes the system more reliable. The second advantage is its implementation on an Android smart-phone, which is readily available to most drivers or cab owners as compared to other general purpose embedded platforms. The third advantage is the use of SMS service to inform the control room as well as the passenger regarding the loss of attention of the driver. The framework provides 93.33% drowsiness state classification as compared to a single stage which gives 86.66%.

Effects of non-driving related tasks in prolonged conditional automated driving – A Wizard of Oz on-road approach in real traffic environment

ObjectiveThe aim of this study was to investigate the effects of non-driving related tasks on the drivers’ fatigue in prolonged conditional automated driving (CAD) in on-road real traffic environment. BackgroundIn CAD, the driving task is executed by the system. This leads to a monotonous situation for the driver as he just has to intervene if requested by the system. Monotony and increasing automation are known causations for fatigue and drowsiness. In previous studies (mostly conducted in driving simulators) an impaired take-over performance due to emerging fatigue and drowsiness could be observed. In the driving simulator studies a rapid increase in drowsiness and fatigue could be observed. To investigate if similar results occur in real traffic environment on-road a Wizard of Oz approach was used. MethodForty-two participants experienced prolonged conditional automated rides on-road in real traffic environment. To provoke fatigue one part of the participants had to engage in a monotonous monitoring task. A control group had no requirements according to the NDRT and had free choice of their activity. Effects on fatigue were measured using percentage of eye-lid closure over time (PERCLOS) and subjective Karolinska Sleepiness Scale (KSS). ResultsProlonged CAD in real traffic environment and simultaneously engaging in a monotonous monitoring task negatively affected the drivers’ state. PERCLOS and subjective KSS significantly increased compared to the control-group. ConclusionFatigue in CAD can emerge in real traffic environment as fast as in driving simulator environments. Especially when participants had to engage in a monotonous monitoring task PERCLOS and KSS rose. ApplicationThe results of this study demonstrate that due to increasing monotony fatigue can emerge in CAD within a one hour drive. The development of fatigue is comparable to the development of fatigue in the driving simulator, where an impaired take-over performance due to fatigue could be observed. Therefore, a monitoring of the driver state and adapted assistance in a take-over situation seems to be a good opportunity to ensure safety in CAD.

Driver Drowsiness Estimation by Parallel Linked Time-Domain CNN with Novel Temporal Measures on Eye States.

This paper presents a vision-based driver drowsiness estimation system from sequences of driver images. We propose a stage-by-stage system instead of an end-to-end system for driver drowsiness estimation. The stage-by-stage system (1) calculates features related to eyes on a frame-by-frame basis, (2) calculates temporal measures on eye states, and (3) estimates drowsiness levels by time-domain convolution with a parallel linked structure. Furthermore, we propose average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS) as novel temporal measures on eye states to extract information related to driver drowsiness. Extensive experiments have been conducted on a driving movie dataset recorded in a real car. Our system achieves a high accuracy of 95.86% and mean absolute error (MAE) of 0.4007 on the dataset.

Non-intrusive Driver Drowsiness Detection based on Face and Eye Tracking

The rate of annual road accidents attributed to drowsy driving are significantly high. Due to this, researchers have proposed several methods aimed at detecting drivers' drowsiness. These methods include subjective, physiological, behavioral, vehicle-based, and hybrid methods. However, recent reports on road safety are still indicating drowsy driving as a major cause of road accidents. This is plausible because the current driver drowsiness detection (DDD) solutions are either intrusive or expensive, thus hindering their ubiquitous nature. This research serves to bridge this gap by providing a test-bed for achieving a non-intrusive and low-cost DDD solution. A behavioral DDD solution is proposed based on tracking the face and eye state of the driver. The aim is to make this research an inception to DDD pervasiveness. To achieve this, National Tsing Hua University (NTHU) Computer Vision Lab's driver drowsiness detection video dataset was utilized. Several video and image processing operations were performed on the videos so as to detect the drivers' eye state. From the eye states, three important drowsiness features were extracted: percentage of eyelid closure (PERCLOS), blink frequency (BF), and Maximum Closure Duration (MCD) of the eyes. These features were then fed as inputs into several machine learning models for drowsiness classification. Models from the K-nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and Artificial Neural Networks (ANN) machine learning algorithms were experimented. These models were evaluated by calculating their accuracy, sensitivity, specificity, miss rate, and false alarm rate values. Although these five metrics were evaluated, the focus was more on getting optimal accuracies and miss rates. The result shows that the best models were a KNN model when k = 31 and an ANN model that used an Adadelta optimizer with 3 hidden layer network of 3, 27, and 9 neurons respective. The KNN model obtained an accuracy of 72.25% with a miss rate of 16.67%, while the ANN model obtained 71.61% and 14.44% accuracy and miss rate respectively.

Hierarchical CNN‐based real‐time fatigue detection system by visual‐based technologies using MSP model

Visual-based technologies are very useful and meaningful to driver's fatigue detection. In this study, the authors present a multi-task hierarchical CNN scheme for fatigue detection system and propose a convolutional neural network (CNN) model with multi-scale pooling (MSP-Net). `Multi-task' includes three tasks: face detection, eye and mouth state detection and fatigue detection. First, they use a pre-trained network - multi-task CNN for face detection extracting eye and mouth regions. Then, the main work of this study, eye and mouth state detection is processed by MSP-Net, which can fit multi-resolution input images captured from variant cameras excellently. For the third step, the percentage of eyelid closure over the pupil over time (PERCLOS) parameters and the frequency of open mouth (FOM) parameters are used to detect fatigue, and the FOM parameters are proposed by ourselves. Besides, they successfully port the system to the embedded platform (the NVIDIA JETSON TX2 development board) and test on real driving scene. The results show that their system performs well and is robust to complex environments and is in line with the demand of real-time system.

An Investigation of Driver Behavior on Urban General Road and in Tunnel Areas

The objective of this study is to examine experience-related differences in microscope driving behavior as drivers performed six separate maneuvers, namely 1) driving on general urban roads, 2) approaching a tunnel portal, 3) driving through a tunnel’s threshold zone, 4) driving in the interior tunnel zone, 5) driving in the zone ahead the tunnel exit and 6) driving after the tunnel exit. An on-road experiment was conducted with 20 drivers in two groups. The first group was made up of new licensed drivers, and the second group contained the more experienced drivers. The study consisted of one between-subject (experience) and five within-subject variables (drive environment type). The drivers’ behavior was measured through Mean Glance Duration, AOI Attention Ration, Horizontal Eye Activity, Vertical Eye Activity, Percentage of Eyelid Closure, and Heart Rate Variability. With respect to the relevant psychological measures, the results show that in general more attention is focused on the far left-hand side of the road and the near front road when driving through tunnel areas when compared with driving on general roads. In addition, the psychological measurements indicate that tunnel’s dark narrow environment causes anxiety on driving for lower heart rate variation coefficient (RRCV). New licensed drivers were more severely affected by the tunnel environment than the experienced drivers.

Experimental study on visual detection for fatigue of fixed-position staff

Fatigue can lead to decreased work performance and poorer safety and health condition. Fatigue is ubiquitous in production and in life, while the research on it is mainly concentrated in the automotive driving, aircraft piloting and other fields, and it is insufficient to study on the fatigue of fixed-position staff. This paper puts forward a non-contact visual image method, which can monitor the extent of fatigue of fixed-position staff. Fatigue threshold used in judgment is obtained by processing the recorded data of visual images of the experimental subjects when fatiguing and by analyzing eye closure time, percentage of eyelid closure (PERCLOS) value, frequency and number of blinks. The results show that there is significant difference among the four indicators before and after experiment subjects undergo fatigue. The fatigue of experimental subjects is obvious when eye closure time is 3.5 s/min, PERCLOS value 6%, and blink frequency 0.4 times/s. This provides a reference for a wider range of detection of fatigue and a method for avoiding mistakes and accidents.

Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State

Driver's fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles (such as buses and heavy trucks) due to prolonged driving periods and boredom in working conditions. In this paper, we propose a vision-based fatigue detection system for bus driver monitoring, which is easy and flexible for deployment in buses and large vehicles. The system consists of modules of head-shoulder detection, face detection, eye detection, eye openness estimation, fusion, drowsiness measure percentage of eyelid closure (PERCLOS) estimation, and fatigue level classification. The core innovative techniques are as follows: 1) an approach to estimate the continuous level of eye openness based on spectral regression; and 2) a fusion algorithm to estimate the eye state based on adaptive integration on the multimodel detections of both eyes. A robust measure of PERCLOS on the continuous level of eye openness is defined, and the driver states are classified on it. In experiments, systematic evaluations and analysis of proposed algorithms, as well as comparison with ground truth on PERCLOS measurements, are performed. The experimental results show the advantages of the system on accuracy and robustness for the challenging situations when a camera of an oblique viewing angle to the driver's face is used for driving state monitoring.

Analysis of driver fatigue causations based on the Bayesian network model

Driver fatigue is the major reason for severe traffic accidents. At present, the driver’s driving state evaluation, based on multi-source information fusion, has become a hotspot in the research field of vehicle safety assistant driving. The purpose of this paper is to build a Bayesian network model for driver fatigue causation analysis considering several visual cues, such as Percentage of Eyelid Closure over the Pupil over Time, Average Eye Closure Speed, etc. The proposed method was divided into three stages, that is, variables analysis, model structure design, and model parameter determination. Finally, the presented model and algorithm were illustrated with a simulation experiment and conclusions were inferred from the experiment data analysis.

Visual Fatigue during Continuous Viewing the 3D Movie

Three-dimensional (3D) displays become more and more popular in many fields. However, visual fatigue is one of the critical factors that impede the wide range of applications of 3D technology. Although many studies have investigated the 3D visual fatigue, a few of them are based on continuous viewing 3D contents. In this paper, we propose a method to evaluate visual fatigue through subjective scoring and objective measuring the physiological parameters during the continuous viewing 3D/2D movie. In the viewing, we test the objective and subjective indicators, including the heart rate (HR), blink frequency (BF) and percentage of eyelid closure over the pupil over time (PERCLOS) and the subjective scoring (SS). Before and after viewing the video, VRT, PMA and questionnaires are measured. Experimental results showed that the subjective score and objective indicates of visual fatigue increased gradually with viewing time although it was fluctuated. The symptoms of visual fatigue were generally more serious after viewing 3D movie than 2D ones. Based on the results above, a model was built to predict visual fatigue from HR and BF during continuous viewing 3D video processes.

Driver drowsiness detection based on non-intrusive metrics considering individual specifics

ObjectivesDrowsy driving is a serious highway safety problem. If drivers could be warned before they became too drowsy to drive safely, some drowsiness-related crashes could be prevented. The presentation of timely warnings, however, depends on reliable detection. To date, the effectiveness of drowsiness detection methods has been limited by their failure to consider individual differences. The present study sought to develop a drowsiness detection model that accommodates the varying individual effects of drowsiness on driving performance. MethodsNineteen driving behavior variables and four eye feature variables were measured as participants drove a fixed road course in a high fidelity motion-based driving simulator after having worked an 8-h night shift. During the test, participants were asked to report their drowsiness level using the Karolinska Sleepiness Scale at the midpoint of each of the six rounds through the road course. A multilevel ordered logit (MOL) model, an ordered logit model, and an artificial neural network model were used to determine drowsiness. ResultsThe MOL had the highest drowsiness detection accuracy, which shows that consideration of individual differences improves the models’ ability to detect drowsiness. According to the results, percentage of eyelid closure, average pupil diameter, standard deviation of lateral position and steering wheel reversals was the most important of the 23 variables. ConclusionThe consideration of individual differences on a drowsiness detection model would increase the accuracy of the model's detection accuracy.

Driver Fatigue Detection System Based on DM3730

Driver fatigue is a popular problem which has attracted people's views. Many research departments are re- searching driver fatigue detection in order to improve the traffic safety. This paper presented a driver fatigue detection system based on DM3730. The system calculated the inter-image difference between frames captured by near-infrared light irradiation, which included identification of the eyes by Otsu adaptive threshold segmentation method and prediction the orientation of the eye in nearby images by Kalman filter. Then the system determined the state of fatigue by improved PERCLOS (Percentage of Eyelid Closure over the Pupil)method. Experimental results show that the system has the ad- vantages of small size and low power consumption. Meanwhile it meets the requirements of all-weather, real-time moni- toring. The system can be extended to automobile and other production processes which the fatigue monitoring is re- quired.

A Review on Driver Face Monitoring Systems for Fatigue and Distraction Detection

Every year, many car accidents due to driver fatigue and distraction occur around the world and cause many casualties and injuries. Driver face monitoring systems is one of the main approaches for driver fatigue or distraction detection and accident prevention. Driver face monitoring systems capture the images from driver face and extract the symptoms of fatigue and distraction from eyes, mouth and head. These symptoms are usually percentage of eyelid closure over time (PERCLOS), eyelid distance, eye blink rate, blink speed, gaze direction, eye saccadic movement, yawning, head nodding and head orientation. The system estimates driver alertness based on extracted symptoms and alarms if needed. In this paper, after an introduction to driver face monitoring systems, the general structure of these systems is discussed. Then a comprehensive review on driver face monitoring systems for fatigue and distraction detection is presented.

Optical Flow and Driver’s Kinematics Analysis for State of Alert Sensing

Road accident statistics from different countries show that a significant number of accidents occur due to driver's fatigue and lack of awareness to traffic conditions. In particular, about 60% of the accidents in which long haul truck and bus drivers are involved are attributed to drowsiness and fatigue. It is thus fundamental to improve non-invasive systems for sensing a driver's state of alert. One of the main challenges to correctly resolve the state of alert is measuring the percentage of eyelid closure over time (PERCLOS), despite the driver's head and body movements. In this paper, we propose a technique that involves optical flow and driver's kinematics analysis to improve the robustness of the driver's alert state measurement under pose changes using a single camera with near-infrared illumination. The proposed approach infers and keeps track of the driver's pose in 3D space in order to ensure that eyes can be located correctly, even after periods of partial occlusion, for example, when the driver stares away from the camera. Our experiments show the effectiveness of the approach with a correct eyes detection rate of 99.41%, on average. The results obtained with the proposed approach in an experiment involving fifteen persons under different levels of sleep deprivation also confirm the discriminability of the fatigue levels. In addition to the measurement of fatigue and drowsiness, the pose tracking capability of the proposed approach has potential applications in distraction assessment and alerting of machine operators.