Search Results (506)

Eccentric photorefractometry is widely used to measure eye refraction, accommodation, gaze position, and pupil size. While the individual calibration of refraction and accommodation data has been extensively studied, gaze measurements have received less attention. PowerRef 3 does not incorporate individual calibration for gaze measurements, resulting in a divergent offset between the measured and expected gaze positions. To address this, we proposed two methods to calculate the physiological vergence angle based on the visual vergence data obtained from PowerRef 3. Twenty-three participants aged 25 ± 4 years viewed Maltese cross stimuli at distances of 25, 30, 50, 70, and 600 cm. The expected vergence angles were calculated considering the individual interpupillary distance at far. Our results demonstrate that the PowerRef 3 gaze data deviated from the expected vergence angles by 9.64 ± 2.73° at 25 cm and 9.25 ± 3.52° at 6 m. The kappa angle calibration method reduced the discrepancy to 3.93 ± 1.19° at 25 cm and 3.70 ± 0.36° at 600 cm, whereas the linear regression method further improved the accuracy to 3.30 ± 0.86° at 25 cm and 0.26 ± 0.01° at 600 cm. Both methods improved the gaze results, with the linear regression calibration method showing greater overall accuracy. Full article

(1) Background: As digital technology continues to reshape visual landscapes, understanding how design elements influence customer experience has become essential. Eye-tracking technology offers a powerful, quantitative approach to assessing visibility, aesthetics, and design components, providing unique insights into visual engagement. (2) Methods: This paper presents a systematic review of eye-tracking methodologies applied in design research. Thirty studies were selected for analysis from recognized academic databases using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Employing the Population, Intervention, Comparison, and Outcomes (PICO) framework, this review focused on experimental studies in visual communication and new media design that utilized visual symbols for communication and leveraged new media technologies. (3) Results: The findings corroborated that eye-tracking technology offers in-depth insights into gaze patterns, visual perception, and attention, which can inform design strategies. This review shows that assessing visual designs based on eye-tracking data can enhance consumer-centered interfaces, better align with user preferences, and foster more engaged behaviors in both digital and physical environments. (4) Conclusions: This review deepens our understanding of the cognitive and emotional processes underlying visual engagement. It also suggests new avenues for integrating diverse eye-tracking metrics into design evaluation, offering practical applications for improving design strategies and advancing the field of design research. Full article

This research project is aimed at exploring the cognitive and emotional processes involved in perceiving Etruscan artifacts. The case study is the Sarcophagus of the Spouses at the National Etruscan Museum in Rome, one of the most important masterpieces in pre-Roman art. The study utilized AI and eye-tracking technology to analyze how viewers engaged with the Etruscan Sarcophagus of the Spouses, revealing key patterns of visual attention and engagement. OpenAI, ChatGPT-4 (accessed on 12 October 2024) was used in conjunction with Colab–Python in order to elaborate all the spreadsheets and data arising from the eye-tracking recording. The results showed that viewers primarily focused on the central figures, especially on their faces and hands, indicating a high level of interest in the human elements of the artifact. The longer fixation duration on these features suggest that viewers find them particularly engaging, which is likely due to their detailed craftsmanship and symbolic significance. The eye-tracking data also highlighted specific gaze patterns, such as diagonal scanning across the sarcophagus, which reflects the composition’s ability to guide viewer attention strategically. The results indicate that viewer focus centers on human elements, especially on faces and hands, suggesting that these features hold both esthetic and symbolic significance. Full article

Assessing handling qualities is crucial for ensuring the safety and operational efficiency of aircraft control characteristics. The growing interest in Urban Air Mobility (UAM) has increased the focus on electric Vertical Takeoff and Landing (eVTOL) aircraft; however, a comprehensive assessment of eVTOL handling qualities remains a challenge. This paper proposed a handling qualities framework to assess eVTOL handling qualities, integrating pilot compensation, task performance, and qualitative comments. An experiment was conducted, where eye-tracking data and subjective ratings from 16 participants as they performed various Mission Task Elements (MTEs) in an eVTOL simulator were analyzed. The relationship between pilot compensation and task workload was investigated based on eye metrics. Data mining results revealed that pilots’ eye movement patterns and workload perception change when performing Mission Task Elements (MTEs) that involve aircraft deficiencies. Additionally, pupil size, pupil diameter, iris diameter, interpupillary distance, iris-to-pupil ratio, and gaze entropy are found to be correlated with both handling qualities and task workload. Furthermore, a handling qualities and pilot workload recognition model is developed based on Long-Short Term Memory (LSTM), which is subsequently trained and evaluated with experimental data, achieving an accuracy of 97%. A case study was conducted to validate the effectiveness of the proposed framework. Overall, the proposed framework addresses the limitations of the existing Handling Qualities Rating Method (HQRM), offering a more comprehensive approach to handling qualities assessment. Full article

Eye-tracking technology can assist researchers in understanding motivational decision-making and choice processes by analysing consumers’ gaze behaviour. Previous studies showed that attention is related to decision, as the preferred stimulus is generally the most observed and the last visited before a decision is made. In this work, the relationship between gaze behaviour and decision-making was explored using eye-tracking technology. Images of six wardrobes incorporating different sustainable design strategies were presented to 57 subjects, who were tasked with selecting the wardrobe they intended to keep the longest. The amount of time spent looking was higher when it was the chosen version. Detailed analyses of gaze plots and heat maps derived from eye-tracking records were employed to identify different patterns of gaze behaviour during the selection process. These patterns included alternating attention between a few versions or comparing them against a reference, allowing the identification of stimuli that initially piqued interest but were ultimately not chosen, as well as potential doubts in the decision-making process. These findings suggest that doubts that arise before making a selection warrant further investigation. By identifying stimuli that attract attention but are not chosen, this study provides valuable insights into consumer behaviour and decision-making processes. Full article

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by differences in social communication and repetitive behaviors, often associated with atypical visual attention patterns. In this paper, the Gaze-Based Autism Classifier (GBAC) is proposed, which is a Deep Neural Network model that leverages both data distillation and data attribution techniques to enhance ASD classification accuracy and explainability. Using data sampled by eye tracking sensors, the model identifies unique gaze behaviors linked to ASD and applies an explainability technique called TracIn for data attribution by computing self-influence scores to filter out noisy or anomalous training samples. This refinement process significantly improves both accuracy and computational efficiency, achieving a test accuracy of 94.35% while using only 77% of the dataset, showing that the proposed GBAC outperforms the same model trained on the full dataset and random sample reductions, as well as the benchmarks. Additionally, the data attribution analysis provides insights into the most influential training examples, offering a deeper understanding of how gaze patterns correlate with ASD-specific characteristics. These results underscore the potential of integrating explainable artificial intelligence into neurodevelopmental disorder diagnostics, advancing clinical research by providing deeper insights into the visual attention patterns associated with ASD. Full article

Bi-directional gaze-based communication offers an intuitive and natural way for users to interact with systems. This approach utilizes the user’s gaze not only to communicate intent but also to obtain feedback, which promotes mutual understanding and trust between the user and the system. In this review, we explore the state of the art in gaze-based communication, focusing on both directions: From user to system and from system to user. First, we examine how eye-tracking data is processed and utilized for communication from the user to the system. This includes a range of techniques for gaze-based interaction and the critical role of intent prediction, which enhances the system’s ability to anticipate the user’s needs. Next, we analyze the reverse pathway—how systems provide feedback to users via various channels, highlighting their advantages and limitations. Finally, we discuss the potential integration of these two communication streams, paving the way for more intuitive and efficient gaze-based interaction models, especially in the context of Artificial Intelligence. Our overview emphasizes the future prospects for combining these approaches to create seamless, trust-building communication between users and systems. Ensuring that these systems are designed with a focus on usability and accessibility will be critical to making them effective communication tools for a wide range of users. Full article

Integrating neuroscience techniques, such as eye tracking, into educational practices has opened new avenues for understanding the cognitive processes underlying learning. This study investigates the feasibility and practicality of using eye tracking as a supportive tool for educators in primary school settings. By taking into account eye-tracking features in lesson plans and instruction, this study explores the benefits and challenges of this technology from teachers’ perspective. The findings reveal that eye tracking can enhance interactivity, maintain student attention, and provide immediate feedback, thereby aiding in identifying student difficulties that may otherwise go unnoticed. However, the study also highlights concerns related to technical complexities, data privacy, and the need for teacher training to utilize and interpret eye-tracking data effectively. These insights contribute to a nuanced understanding of how eye-tracking technology can be implemented in educational settings, offering potential pathways for personalized teaching and improved learning outcomes. Full article

With the deep integration of psychology and artificial intelligence technology and other related technologies, eye control technology has achieved certain results at the practical application level. However, it is found that the accuracy of the current single-modal eye control technology is still not high, which is mainly caused by the inaccurate eye movement detection caused by the high randomness of eye movements in the process of human–computer interaction. Therefore, this study will propose an intent recognition method that fuses facial expressions and eye movement information and expects to complete an eye control method based on the fusion of facial expression and eye movement information based on the multimodal intent recognition dataset, including facial expressions and eye movement information constructed in this study. Based on the self-attention fusion strategy, the fused features are calculated, and the multi-layer perceptron is used to classify the fused features, so as to realize the mutual attention between different features, and improve the accuracy of intention recognition by enhancing the weight of effective features in a targeted manner. In order to solve the problem of inaccurate eye movement detection, an improved YOLOv5 model was proposed, and the accuracy of the model detection was improved by adding two strategies: a small target layer and a CA attention mechanism. At the same time, the corresponding eye movement behavior discrimination algorithm was combined for each eye movement action to realize the output of eye behavior instructions. Finally, the experimental verification of the eye–computer interaction scheme combining the intention recognition model and the eye movement detection model showed that the accuracy of the eye-controlled manipulator to perform various tasks could reach more than 95 percent based on this scheme. Full article

Mobile gaming accounts for more than 50% of global online gaming revenue, surpassing console and browser-based gaming. The success of mobile gaming titles depends on optimizing applications for the specific hardware constraints of mobile devices, such as smaller displays and lower computational power, to maximize battery life. Additionally, these applications must dynamically adapt to the variations in network speed inherent in mobile environments. Ultimately, user engagement and satisfaction are critical, necessitating a favorable comparison to browser and console-based gaming experiences. While Quality of Experience (QoE) subjective evaluations through user surveys are the most reliable method for assessing user perception, various factors, termed influence factors (IFs), can affect user ratings of stimulus quality. This study examines human influence factors in mobile gaming, specifically analyzing the impact of user delight towards displayed content and the effect of gaze tracking. Using Pupil Core eye-tracking hardware, we captured user interactions with mobile devices and measured visual attention. Video stimuli from eight popular games were selected, with resolutions of 720p and 1080p and frame rates of 30 and 60 fps. Our results indicate a statistically significant impact of user delight on the MOS for most video stimuli across all games. Additionally, a trend favoring higher frame rates over screen resolution emerged in user ratings. These findings underscore the significance of optimizing mobile gaming experiences by incorporating models that estimate human influence factors to enhance user satisfaction and engagement. Full article

Gaze zone detection involves estimating where drivers look in terms of broad categories (e.g., left mirror, speedometer, rear mirror). We here specifically focus on the automatic annotation of gaze zones in the context of road safety research, where the system can be tuned to specific drivers and driving conditions, so that an easy to use but accurate system may be obtained. We show with an existing dataset of eye region crops (nine gaze zones) and two newly collected datasets (12 and 10 gaze zones) that image classification with YOLOv8, which has a simple command line interface, achieves near-perfect accuracy without any pre-processing of the images, as long as a model is trained on the driver and conditions for which annotation is required (such as whether the drivers wear glasses or sunglasses). We also present two apps to collect the training images and to train and apply the YOLOv8 models. Future research will need to explore how well the method extends to real driving conditions, which may be more variable and more difficult to annotate for ground truth labels. Full article

Background: Headache is one of the leading causes of disability in the world. Neck proprioception, pain, and postural control are interconnected in both healthy individuals and those with chronic neck pain. This study examines the effects of proprioceptive training using a gaze direction recognition task on postural stability and pain in cervicogenic headache patients. Methods: Patients with cervicogenic headache (n = 34, age: 35–49 y) were randomized into a control group (CON), receiving only selected physical therapy rehabilitation or to an experimental group (EXP), performing proprioceptive training using a gaze direction recognition task plus selected physical therapy rehabilitation. Both programs consisted of 24, 60 min long sessions over 8 weeks. Postural stability was assessed by the modified clinical test of sensory integration of balance (mCTSIB) and a center of pressure test (COP) using the HUMAC balance system. Neck pain was assessed by a visual analog scale. Results: In all six tests, there was a time main effect (p < 0.001). In three of the six tests, there were group by time interactions so that EXP vs. CON improved more in postural stability measured while standing on foam with eyes closed normalized to population norms, COP velocity, and headache (all p ≤ 0.006). There was an association between the percent changes in standing on foam with eyes closed normalized to population norms and percent changes in COP velocity (r = 0.48, p = 0.004, n = 34) and between percent changes in COP velocity and percent changes in headache (r = 0.44, p = 0.008, n = 34). Conclusions: While we did not examine the underlying mechanisms, proprioceptive training in the form of a gaze direction recognition task can improve selected measures of postural stability, standing balance, and pain in cervicogenic headache patients. Full article

Humans have the unique ability to discern spatial and temporal regularities in their surroundings. However, the effect of learning these regularities on eye movement characteristics has not been studied enough. In the present study, we investigated the effect of the frequency of occurrence and the presence of common chunks in visual images on eye movement characteristics like the fixation duration, saccade amplitude and number, and gaze number across sequential experimental epochs. The participants had to discriminate the patterns presented in pairs as the same or different. The order of pairs was repeated six times. Our results show an increase in fixation duration and a decrease in saccade amplitude in the sequential epochs, suggesting a transition from ambient to focal information processing as participants acquire knowledge. This transition indicates deeper cognitive engagement and extended analysis of the stimulus information. Interestingly, contrary to our expectations, the saccade number increased, and the gaze number decreased. These unexpected results might imply a reduction in the memory load and a narrowing of attentional focus when the relevant stimulus characteristics are already determined. Full article

Background/Objectives: The development of techniques for assessing cognitive functions using eye tracking is particularly important for children with developmental disabilities. In this paper, we present pilot results from the validation of two methods for assessing voluntary attention based on eye tracking. Methods: The study involved 80 children aged 3 to 8 years with neurodevelopmental disorders. Children performed two eye-tracking tests in which they had to ‘catch’ a stimulus by looking at it. They also completed the Attention Sustained subtest of the Leiter-3 International Performance Scale. In the first test, the stimuli were presented at different locations on the screen in subtests with stimuli onset asynchrony of 2 s and 1 s. A translucent blue marker marked the position of the gaze on the screen. The number of trials in which the gaze marker approached the stimulus was determined. In the second test, the location of the stimuli on the screen was changed based on gaze fixation in the ROI area. The time taken to complete the task was evaluated. Results: The results of both eye-tracking tests showed significant correlations with scores on the Attention Sustained Leiter-3 subtest and significant test–retest reliability. Conclusions: The results indicate that the present eye-tracking tests can be used for assessing voluntary attention in children with some neurodevelopmental disorders, and further research is warranted to assess the feasibility of these tests for a broader range of developmental disorders. Our findings could have practical implications for the early intervention and ongoing monitoring of attention-related issues. Full article

This paper presents a novel approach to predicting shot accuracy in badminton by analyzing Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop a neural network model that combines visual data from eye-tracking devices with biomechanical data such as body posture and shuttlecock trajectory. Our model is designed to predict shot accuracy, providing insights into the role of QE in performance. The study involved 30 badminton players of varying skill levels from the Chinese Swimming Club in Singapore. Using a combination of eye-tracking technology and motion capture systems, we collected data on QE metrics and biomechanical factors during a series of badminton shots for a total of 750. Key results include: (1) The neural network model achieved 85% accuracy in predicting shot outcomes, demonstrating the potential of integrating QE metrics with biomechanical data. (2) QE duration and onset were identified as the most significant predictors of shot accuracy, followed by racket speed and wrist angle at impact. (3) Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms). (4) A strong positive correlation (r = 0.72) was found between QE duration and shot accuracy across all skill levels. These findings have important implications for badminton training and performance evaluation. The study suggests that QE-based training programs could significantly enhance players’ shot accuracy. Furthermore, the predictive model developed in this study offers a framework for real-time performance analysis and personalized training regimens in badminton. By bridging cognitive neuroscience and sports performance through advanced data analytics, this research paves the way for more sophisticated, individualized training approaches in badminton and potentially other fast-paced sports. Future research directions include exploring the temporal dynamics of QE during matches and developing real-time feedback systems based on QE metrics. Full article