Accuracy Of Eye Tracking Research Articles

Wearable Biosensor Smart Glasses Based on Augmented Reality and Eye Tracking.

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user's intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system's real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system's ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses' algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management.

Integrating session recording and eye-tracking: development and evaluation of a Chrome extension for user behavior analysis

The subject of this article is the development, implementation, and evaluation of a Chrome extension designed to record and replay web sessions using integrated eye-tracking data. The goal is to develop a tool that enhances user interaction analysis by combining session recordings with eye-tracking capabilities. The tasks to be solved are as follows: create a functional Chrome extension that utilizes rrweb for session recording and WebGazer.js for eye tracking; implement features, such as session recording, replay with eye-tracking data overlays, and session management; and provide options for exporting and importing recorded sessions. The methods used are: architectural modeling using UML diagrams to design the system architecture, software engineering techniques for developing the extension, integration testing to ensure the smooth operation of combined features, and data preprocessing techniques to prevent redundancy and reduce noise in eye-tracking data. Additionally, a structured user study with detailed questionnaires combining both Likert-scale questions and open-ended responses and feedback analysis were conducted to evaluate usability and gather feedback. The following results were obtained: the extension was successfully developed and evaluated with 25 participants aged 18–35 years in a controlled environment. High usability ratings were obtained, with an average score of 4.5 out of 5 for the session recording, replay, and session management features. However, the eye-tracking feature received a lower rating of 3.8 out of 5 due to occasional inaccuracies in the eye-tracking data. The qualitative feedback indicated the usefulness of the eye-tracking feature and highlighted the need for improved data accuracy. Conclusions. The scientific novelty of this study lies in the integration of session recording and eye tracking within a Chrome extension, which represents a novel and comprehensive tool for user interaction analysis on the web. The tool's ability to capture both behavioral data and visual attention without requiring website code modifications is particularly valuable for researchers, marketing specialists, UI/UX designers, and product developers. The usability study and feedback analysis provided a clear direction for future improvements, including enhancing the eye-tracking accuracy and integrating advanced analytics and customizable reporting options. Future work will also explore the integration of machine learning algorithms to automatically analyze recorded data to provide deeper insights and actionable recommendations.

Facial Coding Study of the Effect of Halal Claims on Purchase Intention of Electronic Products: Linking Maslow's Hierarchy of Needs Theory

Usability testing plays a pivotal role in assessing user experience on websites, providing businesses with invaluable insights into customer perspectives. Eye-tracking, a widely accepted methodology, zeroes in on visual engagement by analyzing specific Areas of Interest (AOI). This study embarked on an exploration of the impact of AOI on dwell time for the latest iteration of the Waters website and further delineated the sections that captivated the most and least user attention. Adopting a quantitative stance, a two-way ANOVA was deployed on a sample comprising 15 master’s students from two distinct academic institutions. Preliminary findings underscore significant disparities in dwell times across different AOIs, implying that specific AOIs retain user attention more robustly. Notably, the research has its constraints, encompassing potential deviations in webcam-based eye-tracking accuracy, the modest sample size, and post-hoc AOI categorization. As an extension, subsequent studies could probe the nuanced factors swaying dwell time within Waters' website AOIs, integrating methodologies such as the WSD thnique and proactive AOI classification.

AI-Assisted Diagnostics in Dentistry: An Eye-Tracking Study on User Behavior.

Artificial Intelligence (AI) has increasingly been integrated into dental practices, notably in radiographic imaging like Orthopantomograms (OPGs), transforming diagnostic protocols. Eye tracking technology offers a method to understand how dentists' visual attention may differ between conventional and AI-assisted diagnostics, but its integration into daily clinical practice is challenged by the cost and complexity of traditional systems. Thirty experienced practitioners and dental students participated to evaluate the effectiveness of two low-budget eye-tracking systems, including the Peye Tracker (Eye Tracking Systems LTD, Southsea, UK) and Webgazer.js (Brown University, Providence, Rhode Island) in a clinical setting to assess their utility in capturing dentists' visual engagement with OPGs. The hardware and software setup, environmental conditions, and the process for eye-tracking data collection and analysis are illustrated. The study found significant differences in eye-tracking accuracy between the two systems, with Webgazer.js showing higher accuracy compared to Peye Tracker (p<0.001). Additionally, the influence of visual aids (glasses vs. contact lenses) on the performance of eye-tracking systems revealed significant differences for both Peye Tracker (p<0.05) and Webgazer.js (p<0.05). Low-budget eye-tracking devices present challenges in achieving the desired accuracy for analyzing dentists' visual attention in clinical practice, highlighting the need for continued innovation and improvement in this technology. Key words:Artificial intelligence, Eye-tracking device, low-budget, dentistry.

Usability Testing of Waters Brand Promotion Website on University Students

&lt;em&gt;Usability testing plays a pivotal role in assessing user experience on websites, providing businesses with invaluable insights into customer perspectives. Eye-tracking, a widely accepted methodology, zeroes in on visual engagement by analyzing specific Areas of Interest (AOI). This study embarked on an exploration of the impact of AOI on dwell time for the latest iteration of the Waters website and further delineated the sections that captivated the most and least user attention. Adopting a quantitative stance, a two-way ANOVA was deployed on a sample comprising 15 master’s students from two distinct academic institutions. Preliminary findings underscore significant disparities in dwell times across different AOIs, implying that specific AOIs retain user attention more robustly. Notably, the research has its constraints, encompassing potential deviations in webcam-based eye-tracking accuracy, the modest sample size, and post-hoc AOI categorization. As an extension, subsequent studies could probe the nuanced factors swaying dwell time within Waters' website AOIs, integrating methodologies such as the WSD technique and proactive AOI classification.&lt;/em&gt;

Eye-hand coordination all the way: from discrete to continuous hand movements.

The differentiation between continuous and discrete actions is key for behavioral neuroscience. Although many studies have characterized eye-hand coordination during discrete (e.g., reaching) and continuous (e.g., pursuit tracking) actions, all these studies were conducted separately, using different setups and participants. In addition, how eye-hand coordination might operate at the frontier between discrete and continuous movements remains unexplored. Here we filled these gaps by means of a task that could elicit different movement dynamics. Twenty-eight participants were asked to simultaneously track with their eyes and a joystick a visual target that followed an unpredictable trajectory and whose position was updated at different rates (from 1.5 to 240 Hz). This procedure allowed us to examine actions ranging from discrete point-to-point movements (low refresh rate) to continuous pursuit (high refresh rate). For comparison, we also tested a manual tracking condition with the eyes fixed and a pure eye tracking condition (hand fixed). The results showed an abrupt transition between discrete and continuous hand movements around 3 Hz contrasting with a smooth trade-off between fixations and smooth pursuit. Nevertheless, hand and eye tracking accuracy remained strongly correlated, with each of these depending on whether the other effector was recruited. Moreover, gaze-cursor distance and lag were smaller when eye and hand performed the task conjointly than separately. Altogether, despite some dissimilarities in eye and hand dynamics when transitioning between discrete and continuous movements, our results emphasize that eye-hand coordination continues to smoothly operate and support the notion of synergies across eye movement types.NEW & NOTEWORTHY The differentiation between continuous and discrete actions is key for behavioral neuroscience. By using a visuomotor task in which we manipulate the target refresh rate to trigger different movement dynamics, we explored eye-hand coordination all the way from discrete to continuous actions. Despite abrupt changes in hand dynamics, eye-hand coordination continues to operate via a gradual trade-off between fixations and smooth pursuit, an observation confirming the notion of synergies across eye movement types.

Driver Drowsiness Detection using Machine Learning Approach

Driver fatigue is a major cause of traffic accidents. The number of deaths and injuries increases every year around the world. Traffic accidents can be reduced by detecting driver fatigue. This article describes machine learning for sleep detection. Face detection is used to detect the driver's eye area and use this as a reference for eye tracking in subsequent frames. Finally, visual images are used to detect sleep and a warning system is created. This method is divided into three stages: face detection, eye detection, and fatigue detection. Image processing is used to recognize the driver's face and then extract the image of the driver's eyes to detect fatigue. HAAR face detection algorithm outputs the image and then adjusts face detection based on the output. CHT is then used to track the eyes of the visible face. Check the eyes using EAR (Early Evaluation). The proposed system was tested using the proposed system on a Raspberry pi 3 Model B with 1 GB RAM using Logitech HD Webcam C270. According to some video tests, average eye contact and tracking accuracy can reach 95.0%. Therefore, it is a cheaper and better solution for a tired driver to ask to find the road immediately.

Evaluating the Tobii Pro Glasses 2 and 3 in static and dynamic conditions

Over the past few decades, there have been significant developments in eye-tracking technology, particularly in the domain of mobile, head-mounted devices. Nevertheless, questions remain regarding the accuracy of these eye-trackers during static and dynamic tasks. In light of this, we evaluated the performance of two widely used devices: Tobii Pro Glasses 2 and Tobii Pro Glasses 3. A total of 36 participants engaged in tasks under three dynamicity conditions. In the “seated with a chinrest” trial, only the eyes could be moved; in the “seated without a chinrest” trial, both the head and the eyes were free to move; and during the walking trial, participants walked along a straight path. During the seated trials, participants’ gaze was directed towards dots on a wall by means of audio instructions, whereas in the walking trial, participants maintained their gaze on a bullseye while walking towards it. Eye-tracker accuracy was determined using computer vision techniques to identify the target within the scene camera image. The findings showed that Tobii 3 outperformed Tobii 2 in terms of accuracy during the walking trials. Moreover, the results suggest that employing a chinrest in the case of head-mounted eye-trackers is counterproductive, as it necessitates larger eye eccentricities for target fixation, thereby compromising accuracy compared to not using a chinrest, which allows for head movement. Lastly, it was found that participants who reported higher workload demonstrated poorer eye-tracking accuracy. The current findings may be useful in the design of experiments that involve head-mounted eye-trackers.

EyeCoD: Eye Tracking System Acceleration via FlatCam-Based Algorithm and Hardware Co-Design

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Eye tracking has become an essential human-machine interaction modality in virtual and augmented reality (VR/AR) applications requiring high throughput (e.g., &gt;240 FPS), small form-factor, and enhanced visual privacy. Existing eye tracking systems have adopted bulky lens-based cameras, and thus suffer from both a large form-factor and high communication cost between the camera and backend processor. This work presents a camera, algorithm, and accelerator co-designed lensless eye tracking system dubbed EyeCoD, which to the best of our knowledge is the first to provide a general front-end eye tracking solution for AR/VR while satisfying the requirements for both high throughput and smaller form-factor. Specifically, EyeCoD integrates system-, algorithm-, and accelerator-level techniques to boost system efficiency without sacrificing the eye tracking accuracy. We believe that our EyeCoD system will pave the way for next-generation eye tracking solutions in VR/AR and can shed light on future innovations for intelligent imaging systems.</i>

Development of ET-DR Algorithm to Enhance Resolution for Gaze Correction of Low-Resolution Image Based Webcam Eye Tracking

Eye tracking is method of detection of a point on a screen that the user gazes at on CV technology. Eye tracking technology is one of the NUI technologies which enables controlling of GUI or inputting text, but it is also possible to analyze the user’s psychological state or the user’s understanding of the contents being gazed at through the user’s eye gaze analysis. Eye tracking can be used for reading education, internet shopping, commercial advertising, etc. Furthermore, it can be used to detect cheating in online exams. The eye tracking system’s permissible range varies depending not only on the motion of the user, but also on the camera, the quality of the acquired image, and the difficulty of the algorithm. Therefore, the accuracy of eye tracking is greatly influenced by how the eye tracking algorithm is implemented in addition to hardware variables. This paper presents Eye tracking Dead Reckoning (ET-DR) as a method to increase the resolution of the eye-tracking system for reading education. DR is usually an algorithm used in navigation, but it has been modified and optimized to increase the resolution of eye tracking. To verify this algorithm, 100 Korean elementary school kids read Korean texts. At the time, the eye tracking success rate was assessed using a computer with an ET-DR algorithm and a computer without the algorithm, and a highly relevant result was achieved.

A low-cost robotic oculomotor simulator for assessing eye tracking accuracy in health and disease.

Eye tracking accuracy is affected in individuals with vision and oculomotor deficits, impeding our ability to answer important scientific and clinical questions about these disorders. It is difficult to disambiguate decreases in eye movement accuracy and changes in accuracy of the eye tracking itself. We propose the EyeRobot-a low-cost, robotic oculomotor simulator capable of emulating healthy and compromised eye movements to provide ground truth assessment of eye tracker performance, and how different aspects of oculomotor deficits might affect tracking accuracy and performance. The device can operate with eccentric optical axes or large deviations between the eyes, as well as simulate oculomotor pathologies, such as large fixational instabilities. We find that our design can provide accurate eye movements for both central and eccentric viewing conditions, which can be tracked by using a head-mounted eye tracker, Pupil Core. As proof of concept, we examine the effects of eccentric fixation on calibration accuracy and find that Pupil Core's existing eye tracking algorithm is robust to large fixation offsets. In addition, we demonstrate that the EyeRobot can simulate realistic eye movements like saccades and smooth pursuit that can be tracked using video-based eye tracking. These tests suggest that the EyeRobot, an easy to build and flexible tool, can aid with eye tracking validation and future algorithm development in healthy and compromised vision.

Diagnostic accuracy of binocular eye tracking in the assessment of paraphilic disorders

Diagnostic accuracy of binocular eye tracking in the assessment of paraphilic disorders

An interactive eye-tracking system for measuring radiologists' visual fixations in volumetric CT images: Implementation and initial eye-tracking accuracy validation.

Eye-tracking approaches have been used to understand the visual search process in radiology. However, previous eye-tracking work in computer tomography (CT) has been limited largely to single cross-sectional images or video playback of the reconstructed volume, which do not accurately reflect radiologists' visual search activities and their interactivity with three-dimensional image data at a computer workstation (e.g., scroll, pan, and zoom) for visual evaluation of diagnostic imaging targets. We have developed a platform that integrates eye-tracking hardware with in-house-developed reader workstation software to allow monitoring of the visual search process and reader-image interactions in clinically relevant reader tasks. The purpose of this work is to validate the spatial accuracy of eye-tracking data using this platform for different eye-tracking data acquisition modes. An eye-tracker was integrated with a previously developed workstation designed for reader performance studies. The integrated system captured real-time eye movement and workstation events at 1000Hz sampling frequency. The eye-tracker was operated either in head-stabilized mode or in free-movement mode. In head-stabilized mode, the reader positioned their head on a manufacturer-provided chinrest. In free-movement mode, a biofeedback tool emitted an audio cue when the head position was outside the data collection range (general biofeedback) or outside a narrower range of positions near the calibration position (strict biofeedback). Four radiologists and one resident were invited to participate in three studies to determine eye-tracking spatial accuracy under three constraint conditions: head-stabilized mode (i.e., with use of a chin rest), free movement with general biofeedback, and free movement with strict biofeedback. Study 1 evaluated the impact of head stabilization versus general or strict biofeedback using a cross-hair target prior to the integration of the eye-tracker with the image viewing workstation. In Study 2, after integration of the eye-tracker and reader workstation, readers were asked to fixate on targets that were randomly distributed within a volumetric digital phantom. In Study 3, readers used the integrated system to scroll through volumetric patient CT angiographic images while fixating on the centerline of designated blood vessels (from the left coronary artery to dorsalis pedis artery). Spatial accuracy was quantified as the offset between the center of the intended target and the detected fixation using units of image pixels and the degree of visual angle. The three head position constraint conditions yielded comparable accuracy in the studies using digital phantoms. For Study 1 involving the digital crosshairs, the median ± the standard deviation of offset values among readers were 15.2 ± 7.0 image pixels with the chinrest, 14.2 ± 3.6 image pixels with strict biofeedback, and 19.1 ± 6.5 image pixels with general biofeedback. For Study 2 using the random dot phantom, the median ± standard deviation offset values were 16.7 ± 28.8 pixels with use of a chinrest, 16.5 ± 24.6 pixels using strict biofeedback, and 18.0 ± 22.4 pixels using general biofeedback, which translated to a visual angle of about 0.8° for all three conditions. We found no obvious association between eye-tracking accuracy and target size or view time. In Study 3 viewing patient images, use of the chinrest and strict biofeedback demonstrated comparable accuracy, while the use of general biofeedback demonstrated a slightly worse accuracy. The median ± standard deviation of offset values were 14.8 ± 11.4 pixels with use of a chinrest, 21.0 ± 16.2 pixels using strict biofeedback, and 29.7 ± 20.9 image pixels using general biofeedback. These corresponded to visual angles ranging from 0.7° to 1.3°. An integrated eye-tracker system to assess reader eye movement and interactive viewing in relation to imaging targets demonstrated reasonable spatial accuracy for assessment of visual fixation. The head-free movement condition with audio biofeedback performed similarly to head-stabilized mode.

Evaluation of eye tracking for a decision support application.

Eye tracking is used widely to investigate attention and cognitive processes while performing tasks in electronic medical record (EMR) systems. We explored a novel application of eye tracking to collect training data for a machine learning-based clinical decision support tool that predicts which patient data are likely to be relevant for a clinical task. Specifically, we investigated in a laboratory setting the accuracy of eye tracking compared to manual annotation for inferring which patient data in the EMR are judged to be relevant by physicians. We evaluated several methods for processing gaze points that were recorded using a low-cost eye-tracking device. Our results show that eye tracking achieves accuracy and precision of 69% and 53%, respectively compared to manual annotation and are promising for machine learning. The methods for processing gaze points and scripts that we developed offer a first step in developing novel uses for eye tracking for clinical decision support.

Emotion Recognition Performance in Children with Callous Unemotional Traits is Modulated by Co-occurring Autistic Traits

ABSTRACT Objective Atypical emotion recognition (ER) is characteristic of children with high callous unemotional (CU) traits. The current study aims to 1) replicate studies showing ER difficulties for static faces in relation to high CU-traits; 2) test whether ER difficulties remain when more naturalistic dynamic stimuli are used; 3) test whether ER performance for dynamic stimuli is moderated by eye-gaze direction and 4) assess the impact of co-occurring autistic traits on the association between CU and ER. Methods Participants were 292 (152 male) 7-year-olds from the Wirral Child Health and Development Study (WCHADS). Children completed a static and dynamic ER eye-tracking task, and accuracy, reaction time and attention to the eyes were recorded. Results Higher parent-reported CU-traits were significantly associated with reduced ER for static expressions, with lower accuracy for angry and happy faces. No association was found for dynamic expressions. However, parent-reported autistic traits were associated with ER difficulties for both static and dynamic expressions, and after controlling for autistic traits, the association between CU-traits and ER for static expressions became non-significant. CU-traits and looking to the eyes were not associated in either paradigm. Conclusion The finding that CU-traits and ER are associated for static but not naturalistic dynamic expressions may be because motion cues in the dynamic stimuli draw attention to emotion-relevant features such as eyes and mouth. Further, results suggest that ER difficulties in CU-traits may be due, in part, to co-occurring autistic traits. Future developmental studies are required to tease apart pathways toward the apparently overlapping cognitive phenotype.

Look me in the eye: evaluating the accuracy of smartphone-based eye tracking for potential application in autism spectrum disorder research

BackgroundAvoidance to look others in the eye is a characteristic symptom of Autism Spectrum Disorders (ASD), and it has been hypothesised that quantitative monitoring of gaze patterns could be useful to objectively evaluate treatments. However, tools to measure gaze behaviour on a regular basis at a manageable cost are missing. In this paper, we investigated whether a smartphone-based tool could address this problem. Specifically, we assessed the accuracy with which the phone-based, state-of-the-art eye-tracking algorithm iTracker can distinguish between gaze towards the eyes and the mouth of a face displayed on the smartphone screen. This might allow mobile, longitudinal monitoring of gaze aversion behaviour in ASD patients in the future.ResultsWe simulated a smartphone application in which subjects were shown an image on the screen and their gaze was analysed using iTracker. We evaluated the accuracy of our set-up across three tasks in a cohort of 17 healthy volunteers. In the first two tasks, subjects were shown different-sized images of a face and asked to alternate their gaze focus between the eyes and the mouth. In the last task, participants were asked to trace out a circle on the screen with their eyes. We confirm that iTracker can recapitulate the true gaze patterns, and capture relative position of gaze correctly, even on a different phone system to what it was trained on. Subject-specific bias can be corrected using an error model informed from the calibration data. We compare two calibration methods and observe that a linear model performs better than a previously proposed support vector regression-based method.ConclusionsUnder controlled conditions it is possible to reliably distinguish between gaze towards the eyes and the mouth with a smartphone-based set-up. However, future research will be required to improve the robustness of the system to roll angle of the phone and distance between the user and the screen to allow deployment in a home setting. We conclude that a smartphone-based gaze-monitoring tool provides promising opportunities for more quantitative monitoring of ASD.

Implementation System of Human Eye Tracking Algorithm Based on FPGA

With the high-speed development of transportation industry, highway traffic safety has become a considerable problem. Meanwhile, with the development of embedded system and hardware chip, in recent years, human eye detection eye tracking and positioning technology have been more and more widely used in man-machine interaction, security access control and visual detection.In this paper, the high parallelism of FPGA was utilized to realize an elliptical approximate real-time human eye tracking system, which was achieved by the series register structure and random sample consensus (RANSAC), thus improving the speed of image processing without using external memory. Because eye images acquired by the camera often generate a lot of noises due to uneven light and dark background, the preprocessing technologies such as color conversion, image filtering, histogram modification and image sharpening were adopted. In terms of feature extraction of images, the eye tracking algorithm in this paper adopted seven-section rectangular eye tracking characteristic method, which increased a section between the mouth and the nose on the basis of the traditional six-section method, so its recognition accuracy is much higher. It is convenient for the realization of hardware parallel system in FPGA. Finally, aiming at the accuracy and real-time performance of the design system, a more comprehensive simulation test was carried out.The human eye tracking system was verified on DE2-115 multimedia development platform, and the performance of VGA (resolution: 640×480) images of 8-bit grayscale was tested. The results showed that the detection speed of this system was about 47 frames per second under the condition that the detection rate of human face (front face, no inclination) was 93%, which reached the real-time detection level. Additionally, the accuracy of eye tracking based on FPGA system was more than 95%, and it has achieved ideal results in real-time performance and robustness.

Photosensor Oculography: Survey and Parametric Analysis of Designs Using Model-Based Simulation

This paper presents a renewed overview of photosensor oculography (PSOG), an eye-tracking technique based on the use of simple photosensors (usually infrared) to measure the overall amount of reflected light while the eye rotates. PSOG can provide high spatial and temporal resolution, low tracking latency, and reduced power consumption. The deeper examination of this technique is particularly important, given the current needs for efficient eye-tracking mechanisms by the emerging interaction devices, e.g., augmented and virtual reality headsets. In our investigation, we employ an adjustable simulation framework to examine the eye-tracking performance when changing the parameters of different PSOG designs. We focus our examination on eye-tracking accuracy and crosstalk, two crucial characteristics for the seamless interaction via eye-tracking. Our experiments demonstrate the parameter values (or ranges) that lead to different levels of performance for accuracy and crosstalk. We also show that the optimization of accuracy and crosstalk is often driven by competing conditions, and thus, we perform a multi-objective optimization procedure and estimate the respective trade-off parameters. Finally, we present the effects from sensor shifts and evaluate the resulting increase in accuracy error. Our results and analysis can be used to facilitate the selection of parameters in future PSOG systems.

Eye tracker accuracy: quantitative evaluation of the invisible eye center location.

We present a new method to evaluate the accuracy of an eye tracker-based eye localization system. Measuring the accuracy of an eye tracker's primary intention, the estimated point of gaze, is usually done with volunteers and a set of fixation points used as ground truth. However, verifying the accuracy of the location estimate of a volunteer's eye center in 3D space is not easily possible. This is because the eye center, the center of corneal curvature, is an intangible point. We evaluate the eye location accuracy by using an eye phantom instead of eyes of volunteers. For this, we developed a testing stage with a realistic artificial eye and a corresponding kinematic model, which we trained with [Formula: see text] data. This enables us to precisely evaluate the eye location estimate of an eye tracker. We show that the proposed testing stage with the corresponding kinematic model is suitable for such a validation. Further, we evaluate a particular eye tracker-based navigation system and show that this system is able to successfully determine the eye center with a mean accuracy of 0.68 mm. We show the suitability of the evaluated eye tracker for eye interventions, using the proposed testing stage and the corresponding kinematic model. The results further enable specific enhancements of the navigation system to potentially get even better results.

An Examination of Recording Accuracy and Precision From Eye Tracking Data From Toddlerhood to Adulthood.

The quantitative assessment of eye tracking data quality is critical for ensuring accuracy and precision of gaze position measurements. However, researchers often report the eye tracker’s optimal manufacturer’s specifications rather than empirical data about the accuracy and precision of the eye tracking data being presented. Indeed, a recent report indicates that less than half of eye tracking researchers surveyed take the eye tracker’s accuracy into account when determining areas of interest for analysis, an oversight that could impact the validity of reported results and conclusions. Accordingly, we designed a calibration verification protocol to augment independent quality assessment of eye tracking data and examined whether accuracy and precision varied between three age groups of participants. We also examined the degree to which our externally quantified quality assurance metrics aligned with those reported by the manufacturer. We collected data in standard laboratory conditions to demonstrate our method, to illustrate how data quality can vary with participant age, and to give a simple example of the degree to which data quality can differ from manufacturer reported values. In the sample data we collected, accuracy for adults was within the range advertised by the manufacturer, but for school-aged children, accuracy and precision measures were outside this range. Data from toddlers were less accurate and less precise than data from adults. Based on an a priori inclusion criterion, we determined that we could exclude approximately 20% of toddler participants for poor calibration quality quantified using our calibration assessment protocol. We recommend implementing and reporting quality assessment protocols for any eye tracking tasks with participants of any age or developmental ability. We conclude with general observations about our data, recommendations for what factors to consider when establishing data inclusion criteria, and suggestions for stimulus design that can help accommodate variability in calibration. The methods outlined here may be particularly useful for developmental psychologists who use eye tracking as a tool, but who are not experts in eye tracking per se. The calibration verification stimuli and data processing scripts that we developed, along with step-by-step instructions, are freely available for other researchers.