Eye State Research Articles

THE APPLICATION OF SPECTRAL ANALYSIS OF EEG DATA FOR THE IDENTIFICATION OF OPEN AND CLOSED EYE STATES

The article examines the analysis of electroencephalogram (EEG) data for the classification of open and closed eye states using the Fast Fourier Transform (FFT). It is shown that this method demonstrates stable recognition accuracy at the level of 70-80% in distinguishing between open- and closed-eye states, demonstrating its effectiveness in classifying biomedical signals. General information about EEG is described, points for their reading, in particular about the “10-10 system”, information about the main types of brain rhythms is given. Modern methods for analyzing EEG data were also reviewed, highlighting three main approaches: spectral analysis, recurrence analysis, and machine learning methods. Software was developed for classification of information presented in the form of EEG time series obtained in the state of open and closed eyes. The software was developed in Python utilizing the PyRQA library.

DCTCS stack classifier-an integrated framework leveraging discrete cosine transformation, cuckoo search algorithm and stacked machine learning models for EEG-based eye state classification

DCTCS stack classifier-an integrated framework leveraging discrete cosine transformation, cuckoo search algorithm and stacked machine learning models for EEG-based eye state classification

Real-Time Fatigue Detection Algorithms Using Machine Learning for Yawning and Eye State.

Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on convolutional neural networks (CNNs). The device analyses video data recorded from an in-vehicle camera to monitor drivers' facial expressions and detect fatigue indicators such as yawning and eye states. The system is built on a strong architecture and was trained using a diversified dataset under varying lighting circumstances and facial angles. It uses Haar cascade classifiers for facial area extraction and advanced image processing algorithms for fatigue diagnosis. The results demonstrate that the system obtained a 96.54% testing accuracy, demonstrating the efficiency of using behavioural indicators such as yawning frequency and eye state detection to improve performance. The findings show that CNN-based architectures can address major public safety concerns, such as minimizing accidents caused by drowsy driving. This study not only emphasizes the need of deep learning in establishing dependable and practical driver monitoring systems, but it also lays the groundwork for future improvements, such as the incorporation of new behavioural and physiological measurements. The suggested solution is a big step towards increasing road safety and reducing the risks associated with driver weariness.

Head-mounted displays based on human eye condition variation patterns

In response to the practical demands for a wide field of view (FOV), long exit pupil distance, and high image quality in head-mounted displays, an initial structure was built based on the principles of catadioptric lenses in a folded optical path (pancake optics) and the field curvature correction formula. Subsequently, using even-order aspheric surfaces, a VR optical system was developed with a FOV of 2w=110∘, an exit pupil distance of 15 mm, and an exit pupil diameter of 8 mm. Compared to traditional designs, a human eye model was introduced as the image plane to construct a VR–human eye optical system analysis model, allowing for direct analysis of the retinal image quality under different states of the human eye. After that, based on medical statistical data, the impact of different light intensity conditions on the retinal image quality of the VR optical system was explored. Experimental results showed that changes in light intensity affect the pupil diameter, which, in turn, influences the final image quality of the system. To solve the issues, two solutions were proposed: adjusting the screen distance and changing the lens spacing. Both methods significantly improved the imaging quality on the human retina, with an increase of over 30% in the average MTF value. A comparison of these methods was conducted, providing valuable references for future VR optical system designs.

A171: The Effects of Qigong on Balance and Fall Prevention in Older Adults

Background/Purpose: Falls are the second leading cause of unintentional injury deaths globally, with approximately 684,000 deaths from falls each year, and older adults are at the highest risk of death or serious injury due to imbalance, with risk increasing with age. Coupled with the accelerating global aging process, the safety of the elderly population due to fall imbalance should be taken seriously. Qigong, one of the classic traditional Chinese exercises, is beneficial for improving the physical fitness of the elderly. However, few studies have reported the effect of Qigong on fall risk in older adults. Therefore, we conducted this systematic review to investigate the effects of traditional Chinese Qigong on balance and fall-related factors in the elderly population. Method: Using keywords such as “falls,” “balance function,” “older adults,” and “Qigong,” RCTs related to the influence of Chinese traditional Qigong on balance function and fall prevention among older adults were searched from PubMed, Web of Science, SPORTDiscuss, CNKI, and Wanfang in Spring 2024. Subsequently, literature screening and quality assessment were performed. Results: Ten articles were included in this review. The intervention strategy consisted mainly of Qigong Baduanjin and Wuqinxi. A total of 925 healthy older adults aged 60–80 years were included. The duration of the intervention ranged from 1 to 12 months. The frequency of the intervention ranged from 30–60 minutes of exercise 2–7 times per week. Findings showed that nine studies demonstrated a significant effect on improvement of balance or fall prevention in older adults with traditional Chinese Qigong exercises (all p < 0.05). The one study with no intervention effect on dynamic balance ability may be because of visual change interference in the open eye state. Conclusion/Discussion: Based on the studies reviewed, it seemed that traditional Chinese Qigong was effective in reducing the risk of falls in the elderly. Qigong is mainly used to prevent falls by improving static and dynamic balance, coordination, and lower limb muscle strength in older adults. However, existing studies mostly validate the application effects and lack in-depth mechanism investigation. There is also no clear harmonization of exercise time, frequency, intensity, and systematic regimen of Qigong interventions. Therefore, we recommend researchers conduct more studies to further clarify the potential mechanisms of Qigong for fall prevention and their exercise prescription.

Human Operator Mental Fatigue Assessment Based on Video: ML-Driven Approach and Its Application to HFAVD Dataset

The detection of the human mental fatigue state holds immense significance due to its direct impact on work efficiency, specifically in system operation control. Numerous approaches have been proposed to address the challenge of fatigue detection, aiming to identify signs of fatigue and alert the individual. This paper introduces an approach to human mental fatigue assessment based on the application of machine learning techniques to the video of a working operator. For validation purposes, the approach was applied to a dataset, “Human Fatigue Assessment Based on Video Data” (HFAVD) integrating video data with features computed by using our computer vision deep learning models. The incorporated features encompass head movements represented by Euler angles (roll, pitch, and yaw), vital signs (blood pressure, heart rate, oxygen saturation, and respiratory rate), and eye and mouth states (blinking and yawning). The integration of these features eliminates the need for the manual calculation or detection of these parameters, and it obviates the requirement for sensors and external devices, which are commonly employed in existing datasets. The main objective of our work is to advance research in fatigue detection, particularly in work and academic settings. For this reason, we conducted a series of experiments by utilizing machine learning techniques to analyze the dataset and assess the fatigue state based on the features predicted by our models. The results reveal that the random forest technique consistently achieved the highest accuracy and F1-score across all experiments, predominantly exceeding 90%. These findings suggest that random forest is a highly promising technique for this task and prove the strong connection and association among the predicted features used to annotate the videos and the state of fatigue.

EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm

This article introduces the Modified Al-Biruni Earth Radius (MBER) algorithm, which seeks to improve the precision of categorizing eye states as either open (0) or closed (1). The evaluation of the proposed algorithm was assessed using an available EEG dataset that applied preprocessing techniques, including scaling, normalization, and elimination of null values. The MBER algorithm’s binary format is specifically designed to select features that can significantly enhance the accuracy of classification. The proposed algorithm and competing ones, namely, Al-Biruni Earth Radius (BER), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA) were evaluated using predefined sets of assessment criteria. The statistical analysis employed the ANOVA and Wilcoxon signed-rank tests and assessed the effectiveness and significance of the proposed algorithm compared to the other five algorithms. Furthermore, A series of visual depictions were presented to validate the effectiveness and robustness of the proposed algorithm. Thus, the MBER algorithm outperformed the other optimizers on the majority of the unimodal benchmark functions due to these considerations. Different ML models were used for classification, e.g., DT, RF, KNN, SGD, GNB, SVC, and LR. The KNN model achieved the highest values of Precision (PPV) (0.959425), Negative Predictive Value (NPV) (0.964969), FScore (0.963431), accuracy (0.9612), Sensitivity (0.970578) and Specificity (0.949711). Thus, KNN serves as a fitness function and is optimized by the utilization of Modified Al-Biruni earth radius (MBER). Finally, the accuracy of eye state classification achieved 96.12% using the proposed algorithm.

Fatigue Driving Detection Based on Facial Multi-Information Feature Fusion

Abstract Driver fatigue driving risk detection is one of the important areas of road traffic safety research in China. According to statistics, more than 40% of China’s traffic accidents every year are related to driver fatigue. In order to reduce this risk, this paper proposes a new method based on facial multi-information feature fusion to realize real-time fatigue driving detection. The method includes using the camera to capture the driver’s face image in real time, combining the face recognition in Dlib library and the detection of 68 key points, and implementing the face detection through OpenCV. Further, the opening and closing states of eyes and mouth were analyzed based on EAR and MAR algorithms, and a reasonable threshold was set to judge fatigue behavior. The HPE head pose estimation method is used to judge the fatigue state by considering the pitch Angle and roll Angle information. Finally, the improved rough set theory comprehensive evaluation method is used to carry out decision fusion processing on the feature results, and intuitively display the system detection results, which is convenient to evaluate the fatigue state of the current driver. The experimental results show that the multi-feature fusion detection method has good real-time performance, the accuracy rate is 94.55%, and the fatigue state can be detected quickly and accurately under various driving attitudes.

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

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

Spatial attention U-Net model with Harris hawks optimization for retinal blood vessel and optic disc segmentation in fundus images.

The state of the human eye's blood vessels is a crucial aspect in the diagnosis of ophthalmological illnesses. For many computer-aided diagnostic systems, precise retinal vessel segmentation is an essential job. However, it remains a difficult task due to the intricate vascular system of the eye. Although many different vascular segmentation techniques have already been presented, additional study is still required to address the problem of inadequate segmentation of thin and tiny vessels. In this work, we introduce the Spatial Attention U-Net (SAU-Net) modelwith harris hawks' optimization (HHO), a lightweight network that can be applied as a data augmentation technique to improve the efficiency of the existingannotated samples without the need of thousands of training instances for Retinal Blood Vessel and Optic Disc Segmentation. The SAU-Net-HHO implementation uses a spatially inferred attention map multiplied by the input feature map for adaptive feature enhancement. U-Net convolutional blocks have been replaced with structured dropout blocks in the proposed network to prevent overfitting. Data from both vascular extraction (DRIVE) and structured analysis of the retina (STARE) are used to evaluate SAU-Net-HHO performance. The results show that the proposed SAU-Net-HHO performs well on both datasets. Analysing the obtained results, an average of 98.5% accuracy and Specificity 96.7% was achieved for DRIVE dataset and 97.8% accuracy and specificity 94.5% for STARE dataset. The proposed method yields numerical results with average values that are on par with those of state-of-the-art methods. Visual inspection has revealed that the suggested method can segment thin and tiny vessels with greater accuracy than previous methods. It also demonstrates its potential for real-life clinical application.

Unsupervised Learning of Eye State Prototypes for Semantically Rich Blinking Detection.

Blinking contributes to the health and protection of the eye and also holds potential in the context of muscle or nerve disorder diagnosis. Traditional methods of classifying eye blinking as open or closed are insufficient, as they do not capture medical-relevant aspects like closure speed, duration, or percentage. The issue could be solved by reliably detecting blinking intervals in high-temporal recordings. Our research demonstrates the reliable detection of blinking events through data-driven analysis of the eye aspect ratio. In an unsupervised manner, we establish an eye state prototype to identify blink intervals and measure inter-eye synchronicity between moments of peak closure. Additionally, our research shows that manually defined prototypes yield comparable results. Our results demonstrate inter-eye synchronicity up to 4.16 ms. We anticipate that medical professionals could utilize our methods to identify or define disease-specific prototypes as potential diagnostic tools.

Cascaded learning with transformer for simultaneous eye landmark, eye state and gaze estimation

Eye tracking have garnered attention in human–machine interaction, disease monitoring, biometrics, etc. Existing investigations for eye tracking have predominantly concentrated on individual task for pupil detection or gaze estimation, overlooking the implicit relationships that exist among different tasks for eye tracking. In this work, we introduce a cascaded framework with transformer to collaboratively realize eye landmark detection, eye state detection and gaze estimation. Within our framework, we leverage Transformer to capture long dependencies with explicit eye-related structural information and implicit correlation among different tasks. Furthermore, the proposed cascade iteration framework alternatively optimize each task and boost the overall performance for pupil center, eye state and gaze estimation simultaneously. To address the problem of manual annotation, we further introduce the Control-Eye Diffusion Model (CEDM), a controllable eye image generation method conditioned on a simple contour with structure information. The proposed methods are evaluated on challenging datasets such as GI4E, BioID and MPIIGaze, and the results show that our methods outperform state-of-the-art methods in several tasks.

Factors Affecting Career Choice Decisions of Selected Junior High School Students in Calamba City, Philippines: Input to a Localized Career Guidance Program

High school education is the doorstep towards the future of students, whether they will pursue further education or enter the world of work. The study aimed to determine the factors affecting the career choice decisions of selected junior high school students in Calamba City, Philippines. Using case study as a research design, the study utilized a survey questionnaire to gather responses of 234 junior high school students where data gathered were analyzed using descriptive analysis. Results of the study showed that economic factors, such as financial stability and returns received strong agreement from students, while social factors got the most disagreement. Meanwhile, more than half of the respondents are inclined towards STEM-related degree programs and majority of them eye state university and colleges (SUCs) as prospective schools after graduation. The findings became the basis of the proposed localized career guidance program to further guide these students. Recommendations include revisiting existing policies on career guidance and empowerment of SUCs.

Deep face profiler (DeFaP): Towards explicit, non-restrained, non-invasive, facial and gaze comprehension

Eye tracking and head pose estimation (HPE) have previously lacked reliability, interpretability, and comprehensibility. For instance, many works rely on traditional computer vision methods, which may not perform well in dynamic and realistic environments. Recently, a widespread trend has emerged, leveraging deep learning for HPE specifically framed as a regression task; however, considering the real-time applications, the problem could be better formulated as classification (e.g., left, centre, right head pose and gaze) using a hybrid approach. For the first time, we present a complete facial profiling approach to extract micro and macro facial movement, gaze, and eye state features, which can be used for various applications related to comprehension analysis. The multi-model approach provides discrete human-understandable head pose estimations utilising deep transfer learning, a newly introduced method of head roll calculation, gaze estimation via iris detection, and eye state estimation (i.e., open or closed). Unlike existing works, this approach can automatically analyse the input image or video frame to produce human-understandable binary codes (e.g., eye open or close, looking left or right, etc.) for each facial component (aka face channels). The proposed approach is validated on multiple standard datasets, indicating outperformance compared to existing methods in several aspects, including reliability, generalisation, completeness, and interpretability. This work will significantly impact several diverse domains, including psychological and cognitive tasks with a broad scope of applications, such as in police interrogations and investigations, animal behaviour, and smart applications, including driver behaviour analysis, student attention measurement, and automated camera flashes.

A study on the application of multidimensional feature fusion attention mechanism based on sight detection and emotion recognition in online teaching

Abstract Online teaching is not limited by time, but the problem of low learning efficiency is common. To address this problem, the study proposes an attention mechanism for multidimensional feature fusion, which first detects faces, uses a supervised gradient descent algorithm for face feature point detection, and improves the least-squares ellipse-fitting algorithm to detect the open/closed state of human eyes. The sight detection method is also improved, and the fuzzy inference method is used to identify students’ emotions, and the modules are fused to achieve multidimensional feature fusion attention detection for online teaching. The study found that the average accuracy rate was 84.5% with glasses and 92.0% without glasses. The research method with glasses had an average time consumption of 17 ms, while the method without glasses took 15 ms, indicating higher detection accuracy and faster real-time performance. The improved approach led to higher recognition accuracy and accuracy rate. The detection accuracy of a single feature and the research method was 74.1 and 91.9%, respectively. It shows that the research method helps in the detection of students’ attention in online teaching.

Drowsiness Detection in the Advanced Driver-Assistance System using YOLO V5 Detection Model

The development of Artificial Intelligence (AI) in the automobile industry, particularly in the Advanced Driver-Assistance System (ADAS), has been increasing rapidly in recent years. One of the essential features of ADAS is the drowsiness alert system, which monitors the state of the driver's eyes. This article presents a study on the development of ADAS that focuses on drowsiness detection using deep learning through a Convolutional Neural Network (CNN) approach. The study utilized the YOLO V5 model for object detection, which was trained using custom datasets containing images annotated with two labels: "Mengantuk" (Drowsy) and "Bangun" (Awake) The goal was to recognize whether the driver was drowsy or awake. The results of the study showed that the modified YOLO V5 CNN Model had high accuracy based on evaluation metrics in terms of accuracy, precision, and recall in detecting drowsiness around the area of the eyes, with a Mean Average Precision (mAP) of 99.5% and an F1-Score of 99.8%. For better understanding and visualization, the model was tested using real-time detection through a web camera, using Jetson Nano as the inference device. The model detected drowsiness in real time, with a confidence rate of 80% to 97%

Utilizing portable electroencephalography to screen for pathology of Alzheimer’s disease: a methodological advancement in diagnosis of neurodegenerative diseases

BackgroundThe current biomarker-supported diagnosis of Alzheimer’s disease (AD) is hindered by invasiveness and cost issues. This study aimed to address these challenges by utilizing portable electroencephalography (EEG). We propose a novel, non-invasive, and cost-effective method for identifying AD, using a sample of patients with biomarker-verified AD, to facilitate early and accessible disease screening.MethodsThis study included 35 patients with biomarker-verified AD, confirmed via cerebrospinal fluid sampling, and 35 age- and sex-balanced healthy volunteers (HVs). All participants underwent portable EEG recordings, focusing on 2-minute resting-state EEG epochs with closed eyes state. EEG recordings were transformed into scalogram images, which were analyzed using “vision Transformer(ViT),” a cutting-edge deep learning model, to differentiate patients from HVs.ResultsThe application of ViT to the scalogram images derived from portable EEG data demonstrated a significant capability to distinguish between patients with biomarker-verified AD and HVs. The method achieved an accuracy of 73%, with an area under the receiver operating characteristic curve of 0.80, indicating robust performance in identifying AD pathology using neurophysiological measures.ConclusionsOur findings highlight the potential of portable EEG combined with advanced deep learning techniques as a transformative tool for screening of biomarker-verified AD. This study not only contributes to the neurophysiological understanding of AD but also opens new avenues for the development of accessible and non-invasive diagnostic methods. The proposed approach paves the way for future clinical applications, offering a promising solution to the limitations of advanced diagnostic practices for dementia.

Driver Drowsiness Detection System Based on Eye State Analysis

Driver Drowsiness Detection System Based on Eye State Analysis

Vitreous mobility during the posterior vitreous detachment initiation demonstrated by pseudo-motion optical coherence tomography

PurposeTo document the anatomical changes occurring at the vitreoretinal interface during early posterior vitreous detachment (PVD). MethodsWide-angle and pseudo-motion optical coherence tomography (OCT) were obtained on 27 normal eyes during the early stages of age-related PVD. The wide-angle examination in a static phase consisted of montaged conventional OCT images. GIF-formatted pseudo-animation was generated with OCT images acquired initially in a static eye state, followed by scans immediately after ocular excursions. ResultsInitial stages of PVD included peripheral PVD with (stage 1a) or without (stage 1b) interposed material between the posterior vitreous cortex and the retina, and perifoveal PVD expanding to the periphery (stage 2). All stage 1 PVDs presented anterior to the papillomacular vitreous liquefaction. All eyes demonstrated mobility of the vitreous gel at the anterior face of the vitreous liquefaction; however, the adherent cortical vitreous layer comprising the posterior wall of the lacuna showed no mobility. Mobility of the posterior vitreous cortex was not present in eyes with stage 1a PVD and increased with the progression of the stage of PVD (p = 3.60×10−6). ConclusionDuring nonpathological PVD, macula is protected from tractional insults conveyed by mobile vitreous due to overlying vitreous liquefaction. However, the vitreoretinal interface anterior to the lacunae experiences tractional forces until vitreoretinal separation occurs. These observations reinforce the hypothesis that vitreous liquefaction, vitreous mobility, and vitreoschisis formation contribute to PVD initiation and support that premacular lacunae protect the macula during PVD initiation and early progression.

Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny

This study refines the YOLOv7-tiny model through structured pruning and architectural fine-tuning, specifically for real-time eye state detection. By focusing on enhancing the model’s efficiency, particularly in environments with limited computational resources, this research contributes significantly to advancing driver monitoring systems, where timely and accurate detection of eye states such as openness or closure can prevent accidents caused by drowsiness or inattention. Structured pruning was utilized to simplify the YOLOv7-tiny model, reducing complexity and storage requirements. Subsequent fine-tuning involved adjustments to the model’s width and depth to further enhance processing speed and efficiency. The experimental outcomes reveal a pronounced reduction in storage size, of approximately 97%, accompanied by a sixfold increase in frames per second (FPS). Despite these substantial modifications, the model sustains high levels of precision, recall, and mean average precision (mAP). These improvements indicate a significant enhancement in both the speed and efficiency of the model, rendering it highly suitable for real-time applications where computational resources are limited.