Eye Head Research Articles

The Trail Making Test in Virtual Reality (TMT-VR): The Effects of Interaction Modes and Gaming Skills on Cognitive Performance of Young Adults

Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18–35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes as follows: eye-tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. The TMT-VR demonstrates high usability, acceptability, and user experience among participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations.

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

Toward Intelligent Head Impulse Test: A Goggle-Free Approach Using a Monocular Infrared Camera.

To assess vestibular function, video head impulse test (vHIT) is taken as the gold standard by evaluating the vestibulo-ocular reflex (VOR). However, vHIT requires the patient to wear a specialized head-mounted goggle equipment that needs to be calibrated before each use. For this, we proposed an intelligent head impulse test (iHIT) setting with a monocular infrared camera instead of the head-mounted goggle and contributed correspondingly a video classification approach with deep learning to vestibular function determination. Within the iHIT framework, a monocular infrared camera was set in front of the patient to capture test videos, based on which a dataset DiHIT of HIT video clips was set up. We then proposed a two-stage multi-modal video classification network, trained on the dataset DiHIT, that took as input the eye motion and head motion data extracted from the facial keypoints via HIT clips and outputted the identification of the semicircular canal (SCC) being tested (SCC identification) and determination of VOR abnormality (SCC qualitation). Experiments on this dataset DiHIT showed that it achieved the accuracy of 100% in prediction of SCC identification. Furthermore, it attained predictive accuracies of 84.1% in horizontal and 79.0% in vertical SCC qualitation. Compared with existing video-based HIT, iHIT eliminates goggles, does not require equipment calibration, and achieves complete automation. Furthermore, iHIT will bring more benefits to users due to its low cost and ease of operation. Codes and use case pipeline are available at: https://github.com/dec1st2023/iHIT. 3 Laryngoscope, 2024.

Triphenyl Phosphate Alters Methyltransferase Expression and Induces Genome-Wide Aberrant DNA Methylation in Zebrafish Larvae.

Emerging environmental contaminants, organophosphate flame retardants (OPFRs), pose significant threats to ecosystems and human health. Despite numerous studies reporting the toxic effects of OPFRs, research on their epigenetic alterations remains limited. In this study, we investigated the effects of exposure to 2-ethylhexyl diphenyl phosphate (EHDPP), tricresyl phosphate (TMPP), and triphenyl phosphate (TPHP) on DNA methylation patterns during zebrafish embryonic development. We assessed general toxicity and morphological changes, measured global DNA methylation and hydroxymethylation levels, and evaluated DNA methyltransferase (DNMT) enzyme activity, as well as mRNA expression of DNMTs and ten-eleven translocation (TET) methylcytosine dioxygenase genes. Additionally, we analyzed genome-wide methylation patterns in zebrafish larvae using reduced-representation bisulfite sequencing. Our morphological assessment revealed no general toxicity, but a statistically significant yet subtle decrease in body length following exposure to TMPP and EHDPP, along with a reduction in head height after TPHP exposure, was observed. Eye diameter and head width were unaffected by any of the OPFRs. There were no significant changes in global DNA methylation levels in any exposure group, and TMPP showed no clear effect on DNMT expression. However, EHDPP significantly decreased only DNMT1 expression, while TPHP exposure reduced the expression of several DNMT orthologues and TETs in zebrafish larvae, leading to genome-wide aberrant DNA methylation. Differential methylation occurred primarily in introns (43%) and intergenic regions (37%), with 9% and 10% occurring in exons and promoter regions, respectively. Pathway enrichment analysis of differentially methylated region-associated genes indicated that TPHP exposure enhanced several biological and molecular functions corresponding to metabolism and neurological development. KEGG enrichment analysis further revealed TPHP-mediated potential effects on several signaling pathways including TGFβ, cytokine, and insulin signaling. This study identifies specific changes in DNA methylation in zebrafish larvae after TPHP exposure and brings novel insights into the epigenetic mode of action of TPHP.

Róża (Rosa L.) w polskim ziołolecznictwie ludowym od drugiej połowy XIX do początków XXI wieku

The rose is a versatile plant, it provides “jam and wine”, surrounds us with beauty and a pleasant scent, which in the form of essential oil is also a valuable cosmetic. The flowers, fruits, leaves, roots and galls, and even the soil in which it grew, were used in traditional herbal medicine in rural communities. These raw materials were used to prepare infusions, decoctions, jams, juices, water and alcohol extracts, ointments and smoke, which were used to fumigate the sick person or diseased places. They were used to treat eye diseases, dermatological diseases, diarrhea, dysentery, consumption, fever, cough, ulcers, wounds, bone, tooth, head and eye pain, and even to treat madness. Most often, however, rose was used to treat erysipelas, in Polish the name of the disease and the plant was identical, according to the principle “like cures like”. The multitude of obtained “medicines” and the variety of ways of preparing and using these raw materials indicate the ability of traditional communities to observe, draw conclusions, and also cope with situations when access to medical care was very limited. These observations, often supported by long practice, can inspire us to conduct further research on the medicinal properties of this valuable plant.

Knowing where to go: Spatial memory guides eye and body movements in a naturalistic visual search task.

Most research on visual search has used simple tasks presented on a computer screen. However, in natural situations visual search almost always involves eye, head, and body movements in a three-dimensional (3D) environment. The different constraints imposed by these two types of search tasks might explain some of the discrepancies in our understanding concerning the use of memory resources and the role of contextual objects during search. To explore this issue, we analyzed a visual search task performed in an immersive virtual reality apartment. Participants searched for a series of geometric 3D objects while eye movements and head coordinates were recorded. Participants explored the apartment to locate target objects whose location and visibility were manipulated. For objects with reliable locations, we found that repeated searches led to a decrease in search time and number of fixations and to a reduction of errors. Searching for those objects that had been visible in previous trials but were only tested at the end of the experiment was also easier than finding objects for the first time, indicating incidental learning of context. More importantly, we found that body movements showed changes that reflected memory for target location: trajectories were shorter and movement velocities were higher, but only for those objects that had been searched for multiple times. We conclude that memory of 3D space and target location is a critical component of visual search and also modifies movement kinematics. In natural search, memory is used to optimize movement control and reduce energetic costs.

Exploring Attentive User Interface Input via Raspberry Pi, Based on Face Landmark Detection, Eye Open-Closed Detection and Head Movements Detection

Exploring Attentive User Interface Input via Raspberry Pi, Based on Face Landmark Detection, Eye Open-Closed Detection and Head Movements Detection

Impact of Patient Head Posture on Lens Radiation Exposure During Cerebral Angiography.

Cerebral angiography remains crucial for detailed characterization and preoperative assessments for intracranial aneurysm. Despite its diagnostic importance, cerebral angiography poses challenges due to its invasiveness, the risk of neurological complications, and radiation exposure. To investigate the impact of head posture on lens radiation exposure during cerebral angiography, this study focused on the correlation between radiation doses to the eye lens, head flexion angles, and head size. A retrospective analysis was performed on 20 patients who underwent cerebral angiography for unruptured intracranial aneurysms between October and November 2022. Radiation doses to the lens, which were measured in a prior prospective study using photoluminescent glass dosimeters, were analyzed alongside head flexion angles, anteroposterior (AP) head diameters, and kerma-area product (KAP) to evaluate their correlation with lens radiation exposure. The lateral radiation source is located on the left side of the patients. The cohort consisted of 20 patients (60% female, mean age: 62.3 ± 9.9 years). The radiation dose to the left eye (the eye closer to the x-ray source) was 2.8 times higher than that to the right eye (9.18 ± 3.31 mGy vs. 3.3 ± 0.60 mGy, P < 0.001). A strong positive correlation was observed between the left eye lens dose and head flexion angle (R = 0.815, P < 0.001). While the AP head diameter significantly correlated with the flexion angle, it showed no significant correlation with lens dose. The KAP was inversely correlated with both the left lens dose (R = -0.597, P = 0.005) and the flexion angle (R = -0.689, P < 0.001). Our findings underscore the significant impact of head posture on lens radiation exposure during cerebral angiography. Adjusting head positioning may provide a practical approach to reduce radiation exposure to the lens. Furthermore, it is worth noting that the left lens received more radiation than the right, likely due to the X-ray source being on the left side of the patient. AP = anteroposterior; KAP = kerma-area product, PLD = photoluminescent glass dosimeter.

Feature decomposition-based gaze estimation with auxiliary head pose regression

Recognition and understanding of facial images or eye images are critical for eye tracking. Recent studies have shown that the simultaneous use of facial and eye images can effectively lower gaze errors. However, these methods typically consider facial and eye images as two unrelated inputs, without taking into account their distinct representational abilities at the feature level. Additionally, implicitly learned head pose from highly coupled facial features would make the trained model less interpretable and prone to the gaze-head overfitting problem. To address these issues, we propose a method that aims to enhance task-relevant features while suppressing other noises by leveraging feature decomposition. We disentangle eye-related features from the facial image via a projection module and further make them distinctive with an attention-based head pose regression task, which could enhance the representation of gaze-related features and make the model less susceptible to task-irrelevant features. After that, the mutually separated eye features and head pose are recombined to achieve more accurate gaze estimation. Experimental results demonstrate that our method achieves state-of-the-art performance, with an estimation error of 3.90° on the MPIIGaze dataset and 5.15° error on the EyeDiap dataset, respectively.

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.

Driver Drowsiness Detection using CNN

Abstract: Driver drowsiness is a significant factor contributing to road accidents worldwide, posing a major threat to public safety. This project presents a robust and efficient approach to detecting driver drowsiness using Convolutional Neural Networks (CNN). The proposed system aims to enhance road safety by monitoring drivers in real-time and providing timely warnings to prevent accidents caused by fatigue. The methodology involves capturing video frames of the driver's face using a camera installed in the vehicle. The CNN model is trained on a comprehensive dataset containing images of alert and drowsy states. Key facial landmarks and features, such as eye closure duration, yawning frequency, and head position, are extracted and analyzed to determine the driver's level of alertness. By leveraging the powerful feature extraction capabilities of CNNs, the system can accurately distinguish between alert and drowsy states with high precision. Experimental results demonstrate the effectiveness of the proposed system in real-world scenarios, achieving a high accuracy rate in detecting drowsiness. The system's performance is evaluated against various benchmarks, showcasing its potential for integration into modern vehicles as a preventive safety measure. The deployment of this CNN-based drowsiness detection system can significantly reduce the risk of accidents, contributing to safer driving conditions and saving lives.

Implementation of Engagement Detection for Human-Robot Interaction in Complex Environments.

This study develops a comprehensive robotic system, termed the robot cognitive system, for complex environments, integrating three models: the engagement model, the intention model, and the human-robot interaction (HRI) model. The system aims to enhance the naturalness and comfort of HRI by enabling robots to detect human behaviors, intentions, and emotions accurately. A novel dual-arm-hand mobile robot, Mobi, was designed to demonstrate the system's efficacy. The engagement model utilizes eye gaze, head pose, and action recognition to determine the suitable moment for interaction initiation, addressing potential eye contact anxiety. The intention model employs sentiment analysis and emotion classification to infer the interactor's intentions. The HRI model, integrated with Google Dialogflow, facilitates appropriate robot responses based on user feedback. The system's performance was validated in a retail environment scenario, demonstrating its potential to improve the user experience in HRIs.

Driver fatigue detection method based on facial features using deep learning

Fatigue driving can result in delayed driver reactions and lack of concentration, increasing the risk of traffic accidents. Therefore, finding effective fatigue detection methods is crucial for accident prevention and ensuring public safety. Traditional driver fatigue detection methods based on facial features are limited by their reliance on manually designed features and classifiers, leading to low accuracy, slow detection speed, and inefficient performance. Hence, methods based on deep learning have gradually become a trend in fatigue-driving detection research. This paper focuses on driver fatigue detection methods based on facial features using deep learning. In this study, we analyze numerous research outcomes on how to accurately extract, analyze, and integrate various facial features, such as eye features, mouth features, facial expressions, and head postures, to precisely determine the driver's fatigue state. Then, we compare the latest deep learning-based technologies. This paper provides an information-rich repository to help researchers select more promising designs, that can drive the widespread development of fatigue-driving detection technologies.

DROWSINESS AND ALCOHOL DETECTION USING ARDUINO

Accidents on road ways are increasing in our day to day lives. These accidents pose significant threats to human safety and well-being. There are several ways for reduction of accidents. Some of them are Drowsiness detection and Alcoholic detection. By using sensors, communication devices and algorithms, the system aims to identify potential hazards. This abstract presents a comprehensive approach to accident reduction using Internet of Things (IoT) technology. Drowsiness Detection System aims to mitigate the risks of drowsy driving and enhance road safety. By utilizing an Arduino microcontroller and various sensors, this system continuously monitors the driver’s vital signs and facial expressions. It analyzes parameters such as eye blink patterns, head movements, and heart rate to accurately identify signs of drowsiness. Upon detecting drowsiness, the system promptly alerts the driver through alarms or visual cues, prompting them to take necessary actions to prevent accidents. This project serves as a vital tool in reducing road accidents caused by driver drowsiness, ensuring safer journeys for all. This project presents the design and implementation of an Alcohol Detection with Engine Locking for cars using Arduino UNO as the MCU (Master Control Unit). The system will continuously monitor level of alcohol concentration in alcohol detection sensor and thus turn off the engine of vehicle if the alcohol concentration is above threshold level. The model will also send the message of whereabouts of the vehicle through SIM900A. The project provides an efficient solution to control accidents due to drunk driving Key Words: Arduino UNO, Sensor Fusion, Real time monitoring, MQ-3 sensor, GPS module, GSM module, Telematics.

Driver Drowsiness Detection Using Raspberry Pi

Driver Drowsiness Is A Significant Factor Contributing To Road Accidents Worldwide. This Paper Proposes A Novel Approach To Mitigate This Problem By Developing A Driver Drowsiness Detection System (Ddds) Using Raspberry Pi. The System Utilizes Image Processing Techniques To Monitor The Driver's Facial Features And Detect Signs Of Drowsiness In Real-Time. A Combination Of Computer Vision Algorithms And Models Is Employed To Accurately Identify Fatigue-Related Symptoms Such As Eye Closure And Head Nodding. The Proposed System Offers A Cost-Effective And Efficient Solution For Enhancing Road Safety By Alerting Drivers When They Exhibit Signs Of Drowsiness, Thereby Reducing The Risk Of Accidents. Keywords: Driver Drowsiness Detection, Raspberry Pi, Image Processing, Computer Vision, Road Safety.

The eyes have it! Functional field of view differences between visual search behavior and body-worn camera during a use of force response in active-duty police officers

ABSTRACT Although officer body-worn cameras (BWCs) have improved transparency of police interactions within the community, BWCs have a limited field of view, are subject to bias, and do not account for the factors that influence rapid decision-making by officers, including their visual attentional control and perceptual processes. The purpose of this study was to investigate the camera perspective of six critical incidents and position data from BWC compared to eye tracking and head movement data in a use-of-force scenario from 44 active-duty police officers. The analysis of gyroscope and accelerometer data demonstrated low correlations between eye cameras and BWC position data. Officers attended 80.5% of all critical incidents, whereas BWC view captured only 66.2%, especially missing key events (<48%). BWC footage did not account for the visual information and the behaviors of the suspect, potential threats, and bystanders who influence the officers' decision-making during the use of force encounters.

Pilot Study Assessing the Safety and Acceptance of a Novel Virtual Reality System to Improve Visual Function

ABSTRACT Purpose To assess the feasibility of the clinical use of a novel Virtual Reality (VR) training software designed to be used for active vision therapy in amblyopic patients by determining its preliminary safety and acceptance on the visual function of healthy adults. Methods Pilot study enrolling 10 individuals (3 men, 7 women, mean age: 31.8 ± 6.5 years) with a best-corrected visual acuity (BCVA) of ≥ .90 (decimal) in both eyes were evaluated before and after 20 minutes of exposure to the NEIVATECH VR system using the HTC Vive Pro Eye head mounted display. Visual function assessment included near (40 cm) and distance (6 m) cover test (CT), stereopsis, binocular accommodative facility (BAF), near point of convergence (NPC), near point of accommodation (NPA), accommodative-convergence over accommodation (AC/A) ratio and positive and negative fusional vergences. Safety was assessed using the VR Sickness Questionnaire (VRSQ) and acceptance using the Technology Acceptance Model ;(TAM). Changes in all these variables after VR exposure were analyzed. Results Short-term exposure to the NEIVATECH VR system only induced statistically significant changes in distance phoria (p = .016), but these changes were not clinically relevant. No significant changes were observed in VRSQ oculo-motricity and disorientation scores after exposure (p = .197 and .317, respectively). TAM scores showed a good acceptance of the system in terms of perceived enjoyment and perceived ease of use, although some concerns were raised in relation to the intention-to-use domain. Conclusion Exposure to the NEIVATECH VR system does not seem to adversely affect the visual function in healthy adults and its safety and acceptance profile seems to be adequate for supporting its potential use in other populations, such as amblyopic patients.

Validation of widefield aberrometer for global peripheral aberration scanning

Aberrometers based on Hartmann-Shack wavefront sensing methods have been widely used in the assessment of aberrations in the human eye, a crucial aspect in addressing or adjusting peripheral vision for myopia control, among other applications. Nevertheless, most available aberrometers exhibit limitations such as slow scanning speeds and restricted visual fields when measuring peripheral aberrations. Even the fastest scanning capabilities of commercialized aberrometer are confined to limited visual field and meridians. In this paper, a novel scanning aberrometer for efficient measurement of human eye peripheral aberrations is developed. This instrument allows the measurement of a large visual field angle up to ± 50° and takes all meridian scanning into account, which means it is a widefield aberrometer that can scan the peripheral aberrations globally. Due to the use of the precision motorized rotation stage, both the meridional and visual field angular resolutions of this instrument can be lower to 1°. During the entire measurement process, the subject only needs to focus on a single fixation target. No eye turn or head turn is required. By the instrument installation completed in April 2023, it stands as the only peripheral aberration scanning aberrometer capable of measuring up to a 50° visual field at all meridians using a single fixation target and achieving high angular resolution.

Large eye–head gaze shifts measured with a wearable eye tracker and an industrial camera

We built a novel setup to record large gaze shifts (up to 140∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ $$\\end{document}). The setup consists of a wearable eye tracker and a high-speed camera with fiducial marker technology to track the head. We tested our setup by replicating findings from the classic eye–head gaze shift literature. We conclude that our new inexpensive setup is good enough to investigate the dynamics of large eye–head gaze shifts. This novel setup could be used for future research on large eye–head gaze shifts, but also for research on gaze during e.g., human interaction. We further discuss reference frames and terminology in head-free eye tracking. Despite a transition from head-fixed eye tracking to head-free gaze tracking, researchers still use head-fixed eye movement terminology when discussing world-fixed gaze phenomena. We propose to use more specific terminology for world-fixed phenomena, including gaze fixation, gaze pursuit, and gaze saccade.

Safety and efficacy of dupilumab in adult atopic dermatitis in Singapore.

Dupilumab, a biologic approved for treatment of moderate-to-severe atopic dermatitis (AD), has been reported to be largely effective with minimal adverse effects. However, being a targeted therapy, its effectiveness and side effects are expected to be varied in a heterogeneous ethnic population. Currently, the majority of studies on dupilumab are based on Western populations. A retrospective cohort of 51 patients with moderate-to-severe AD treated with dupilumab at a tertiary skin institute from January 2018 to February 2020 was included. Outcome measures such as Scoring Atopic Dermatitis (SCORAD), Eczema Area and Severity Index (EASI) and Dermatology Quality of Life Index (DLQI) were analysed at baseline and after 16 weeks of treatment. Laboratory tests at the respective time points were also measured. Patients treated with dupilumab showed consistent reduction in body surface area (BSA) involved, SCORAD, EASI and DLQI scores. Concurrently, serum immunoglobulin E (IgE) and absolute eosinophil count showed a downward trend at 4 months of treatment. There was a transient increase in mean serum IgE at 1 month of dupilumab treatment. Also, 42.0% and 35.3% of patients developed eye symptoms, and head and neck dermatitis, respectively, but there were no major adverse events reported. Childhood-onset AD was more likely to have improved BSA scores compared to adult-onset AD. Our study confirms the efficacy of dupilumab with real-world data in an adult Asian population. Higher proportions of eye symptoms, and head and neck dermatitis were observed in our cohort compared to other Asian clinical cohort studies. Further studies are needed to evaluate these differences.