Eye-tracking Tool Research Articles

MoTE: Desarrollo de una Herramienta Informática para el Monitoreo del Compromiso Estudiantil

Student engagement in learning is crucial for academic achievement, motivation, and active participation. However, measuring and accounting for student engagement, both in face-to-face and online sessions, remains a challenge for educators. Existing methods, such as self-reports, interviews, and even real-time eye tracking tools, lack a multidimensional engagement approach (i.e., cognitive, affective, and behavioral). In response, this article presents the development of MoTE, a real-time tool for monitoring student engagement. Following a Design-Based Research methodology, we detail the phases of identification of indicators and visualizations to meet the needs of teachers and students, culminating with the proposal of the architecture, and the implementation of an initial prototype. It ends with the evaluation with 146 students in face-to-face and online contexts, providing valuable information on the indicators, dashboards, and functionalities to design an effective tool for monitoring student engagement. This work not only proposes an innovative approach to assessing engagement but also opens avenues for future research and practical applications in education.

How does visual context of urban open spaces drive soundscape pleasantness and eventfulness: a laboratory experiment using a higher order Ambisonics speaker system and immersive virtual reality

Perception of sound environments is influenced by the context we experience them in. A big portion of the contextual information comes from the visual domain, including our understanding of what a place is but also from its visual features. A laboratory study combining questionnaire responses and eye-tracking tools was designed to investigate if the soundscape outcomes and participants' behaviour inside the simulation can be explained by the perceptual outcomes defined by visual information. 360 degree videos and First Order Ambisonics audio recordings of 27 different urban open spaces taken from the International Soundscape Database were used as the stimuli delivered via a Virtual Reality Head-Mounted Display and a Higher Order Ambisonics speaker array, while a neutral grey environment without sounds being reproduced represented the baseline scenario. A questionnaire tool was deployed within the IVR simulation to collect participants' responses describing their perception of the reproduced environments corresponding to the circumplex model featured in the Method A of the ISO/TS 12913-2. The results revealed a good coverage of the two-dimensional perceptual circumplex space and significant differences between perceptual outcomes driven by sound and those driven by visual stimuli.

Bibliography analysis: The influence of affective design on human cognition using Kansei engineering

There is a lot of research using affective and cognitive aspects as an approach to creating a product design or evaluating a product and service with consumers as the center of the design. Overall, human factors, product design, and affective design are important factors in creating products that are useful and enjoyable to use, by considering all factors, designers can create products that have a chance of success in the market. This research was conducted to look at studies based on variables, objects, and methods based on previous journals. This aims to identify previous research and see opportunities if research is carried out in the future. The method used uses affective and cognitive aspects and uses measurements using eye-tracking and EEG. Many statistical measurements are carried out using ANOVA. Based on the analysis, and interpretation of previous research journals, after identifying each aspect and component, conclusions can be drawn regarding aspects of each journal and future research opportunities, then research can be obtained or carried out using the research object of a product that will be made either commercially or commercially. does not use variables eye tracking and EEG measurement tools to determine the user's response. Then, a stimulus can be given to measure a person's level of cognitive response, using product design images, music, physical products, interfaces, music, etc. which will evoke a response from the person. Apart from that, methods that can be used for further research can use affective and cognitive aspects as methods.

Pistol: Pupil Invisible Supportive Tool in the Wild

This paper is an in the wild evaluation of the eye tracking tool Pistol. Pistol supports Pupil Invisible projects and other eye trackers (Dikablis, Emke GmbH, Look, Pupil, and many more) in offline mode. For all eye tracking recordings, Pistol is able to compute pupil and iris ellipse, eye aperture, eye lids, 3D eye ball, optical vector estimation, and eye movement types. For Pupil Invisible projects, it also supports the detection of a special marker and allows the 2D and 3D gaze estimation mapped on the scene camera. In this extended journal paper for Pistol we focus on challenging in the wild situations, such as reflections on glasses, eye tracking during sport, changing lightning conditions, and this for two eye trackers. The tool Pistol can be downloaded here Link

The gaze characteristics in preterm children: The appropriate timing for an eye-tracking tool

BackgroundAn objective screening tool for autism spectrum disorder (ASD), also known as an eye-tracking tool, assesses the patient’s abnormal gaze patterns and detects the risk of ASD. As this tool is generally used for children born at term, this study aimed to clarify the appropriate timing for using the tool for preterm children, factors that influence the timing, and evaluate their gaze characteristics using the Gazefinder®. MethodIn 90 preterm children, a total of 125 eye-tracking tasks were completed and analyzed in 3–6, 7–9, 10–12, 13–18, and 19–32 months of corrected age (CA). The Gazefinder® was used to compare the mean fixation time percentage (MFP) in each CA and evaluate the gaze patterns. Perinatal factors associated with low MFP were also analyzed. ResultsOnly 50% of the children scored ≥70% MFP at 3–6 months of CA. The MFP increased significantly after 7 months of CA (p = 0.0003), reached 90% at 13–18 months, and 100% at 19–32 months of CA. Chronic lung disease (CLD) was a clinical factor associated with low MFP (p = 0.036). Preterm children gazed more at eyes but gazed at mouths when the mouth moved. ConclusionIt is necessary for preterm children to begin using Gazefinder® atleast at ≥13 months of age, especially those complicated with CLD. Preterm children prefer gazing at social information just as typically developing children.

Examining Visual Attention to Tobacco Marketing Materials Among Young Adult Smokers: Protocol for a Remote Webcam-Based Eye-Tracking Experiment.

Eye tracking provides an objective way to measure attention, which can advance researchers' and policy makers' understanding of tobacco marketing influences. The development of remote webcam-based eye-tracking technology, integrated with web-based crowdsourcing studies, may be a cost-effective and time-efficient alternative to laboratory-based eye-tracking methods. However, research is needed to evaluate the utility of remote eye-tracking methods. This study aimed to detail the process of designing a remote webcam-based eye-tracking experiment and provide data on associations between participant characteristics and the outcomes of experiment completion. A total of 2023 young adult (aged 18-34 years) cigarette smokers in the United States were recruited to complete a web-based survey that included a 90-second remote eye-tracking experiment that examined attention to e-cigarette marketing materials. Primary outcome measures assessed the completion of the remote eye-tracking experiment-specifically, experiment initiated versus not initiated, experiment completed versus not completed, and usable versus nonusable eye-tracking data generated. Multivariable logistic regressions examined the associations between outcome measures and participants' sociodemographic backgrounds, tobacco use history, and electronic devices (mobile vs desktop) used during the experiment. Study recruitment began on April 14, 2022, and ended on May 3, 2022. Of the 2023 survey participants, 1887 (93.28%) initiated the experiment, and 777 (38.41%) completed the experiment. Of the 777 participants who completed the experiment, 381 (49%) generated usable data. Results from the full regression models show that non-Hispanic Black participants (adjusted odds ratio [AOR] 0.64, 95% CI 0.45-0.91) were less likely to complete the eye-tracking experiment than non-Hispanic White participants. In addition, female (vs male) participants (AOR 1.46, 95% CI 1.01-2.11), those currently using (vs not using) e-cigarettes (AOR 2.08, 95% CI 1.13-3.82), and those who used mobile (vs desktop) devices (AOR 5.10, 95% CI 3.05-8.52) were more likely to generate usable eye-tracking data. Young adult participants were willing to try remote eye-tracking technology, and nearly half of those who completed the experiment generated usable eye-tracking data (381/777, 49%). Thus, we believe that the use of remote eye-tracking tools, integrated with crowdsourcing recruitment, can be a useful approach for the tobacco regulatory science research community to collect high-quality, large-scale eye-tracking data in a timely fashion and thereby address research questions related to the ever-evolving tobacco marketing landscape. It would be useful to investigate techniques to enhance completion rates and data usability. RR1-10.2196/43512.

Crowdsourced Online Biometric Studies: Is the juice worth the squeeze?

While promising, online biometric studies with crowdsourced participants have vagaries that can limit success. This paper shares researcher experience with biometric online data collection and a study that compared the differences of online and lab-based eye tracking (ET) and facial expression (FE) data. Students and the public were separated into two groups to place orders from a well-known restaurant online menu application. Group 1 accessed the menu web application routed through an online biometric data collection tool while Group 2 interacted with the menu web application through a desktop application that collected the biometric data. The study found little difference in the user experience metrics, the facial expression data, or the number of gaze points between the two data collection methods. Interestingly and consistent with field experience a significant difference in the ET data was observed. The vagaries of the field present significant hazards for researchers and even more so if study participants are compensated. Recommendations for researchers to optimize research effort are provided regarding recruitment, participant compensation, analysis effort and expertise, maximizing the usable data, and employing eye tracking tools.

Can a chatbot enhance hazard awareness in the construction industry?

Safety training enhances hazard awareness in the construction industry. Its effectiveness is a component of occupational safety and health. While face-to-face safety training has dominated in the past, the frequent lockdowns during COVID-19 have led us to rethink new solutions. A chatbot is messaging software that allows people to interact, obtain answers, and handle sales and inquiries through a computer algorithm. While chatbots have been used for language education, no study has investigated their usefulness for hazard awareness enhancement after chatbot training. In this regard, we developed four Telegram chatbots for construction safety training and designed the experiment as the treatment factor. Previous researchers utilized eye-tracking in the laboratory for construction safety research; most have adopted it for qualitative analyses such as heat maps or gaze plots to study visual paths or search strategies via eye-trackers, which only studied the impact of one factor. Our research has utilized an artificial intelligence-based eye-tracking tool. As hazard awareness can be affected by several factors, we filled this research void using 2-way interaction terms using the design of experiment (DOE) model. We designed an eye-tracking experiment to study the impact of site experience, Telegram chatbot safety training, and task complexity on hazard awareness, which is the first of its kind. The results showed that Telegram chatbot training enhanced the hazard awareness of participants with less onsite experience and in less complex scenarios. Low-cost chatbot safety training could improve site workers' danger awareness, but the design needs to be adjusted according to participants' experience. Our results offer insights to construction safety managers in safety knowledge sharing and safety training.

REAL-TIME EYE TRACKING USING HEAT MAPS

Communication in modern days has developed a lot, including wireless networks, Artificial Intelligence (AI) interaction, and human-computer interfaces. People with paralysis and immobile disorders face daily difficulties communicating with others and gadgets. Eye tracking has proven to promote accessible and accurate interaction compared to other complex automatic interactions. The project aims to develop an electronic eye blinker that integrates with the experimental setup to determine clinical pupil redundancy. The proposed solution comes up with an eye-tracking tool within an inbuilt laptop webcam that tracks the eye’s pupil in the given screen dimensions and generates heat maps on the tracked locations. These heat maps can denote a letter (in case of eye writing), an indication to click on that location (in case of gadget communication), or for blinking analysis. The proposed method achieves a perfect F-measure score of 0.998 to 1.000, which is comparatively more accurate and efficient than the existing technologies. The solution also provides an effective method to determine the eye's refractive error, which can replace the complex refractometers. Further, the spatially tracked coordinates obtained during the experiment can be used to analyze the patient’s blinking pattern, which, in turn, can detect retinal disorders and their progress during medication. One of the applications of the project is to integrate the derived model with a Brain-computer interface system to allow fast communications for the disabled.

Areas of Interest (AOI) on marketing mix elements of green and non-green products in customer decision making

Technological advancements in eye-tracking have enabled the development of interactive experimental setups for studying consumer behaviour. A common method for examining gaze data is Area of Interest (AOI). Therefore, this study fully utilised eye-tracking tools to measure participants' allocation of visual attention to the marketing mix elements in green and non-green products. This is because the product, price, place, and promotion are still the most crucial factors that customers consider when purchasing. The primary objective of this study is to discover and understand the primary marketing function that directly influences customer decision-making from a neuromarketing perspective. Their eye movements were simultaneously registered using SMI Eye Tracking Glasses 2 Wireless, and the gaze locations of participants were measured from AOI. The findings of this study have a significant impact on the importance of eye movements in decision-making, particularly when choosing important marketing elements before purchasing green and non-green products.

The Influence of the Structure on the Attention of Organization Managers

We aim to understand the aspects of the structural distribution of attention that influence the attention of individuals in organizations. We performed two distinct and interlinked studies considered experiments using an eye tracking tool. In the first study, we evaluated the effect of training (as a proxy for the function) on managerial care and the individual characteristics of future managers (n=151). In the second study, we evaluated the effect of function/position on managerial care and the individual characteristics of professionals (n=140) working in organizations. We conclude that the professional profile and the organizational environment (internship) influence the choice of information in the areas of interest (AOI) of future managers. About managers, we concluded that the professional profile influences the choice of information (AOI) for the allocation of care. The initial training of decision-makers (managers) becomes less relevant than their professional profile, which is shaped based on their professional experiences and practices.

Unity Human Eye Model for Gaze Tracking with a Query-Driven Dynamic Vision Sensor.

Objective measurement of gaze pattern and eye movement during untethered activity has important applications for neuroscience research and neurological disease detection. Current commercial eye-tracking tools rely on desk-top devices with infrared emitters and conventional frame-based cameras. Although wearable options do exist, the large power-consumption from their conventional cameras limit true long-term mobile usage. The query-driven Dynamic Vision Sensor (qDVS) is a neuromorphic camera which dramatically reduces power consumption by outputting only intensity-change threshold events, as opposed to full frames of intensity data. However, such hardware has not yet been implemented for on-body eye-tracking, but the feasibility can be demonstrated using a mathematical simulator to evaluate the eye-tracking ca-pabilities of the qDVS under controlled conditions. Specifically, a framework utilizing a realistic human eye model in the 3D graphics engine, Unity, is presented to enable the controlled and direct comparison of image-based gaze tracking methods. Eye-tracking based on qDVS frames was compared against two different conventional frame eye-tracking methods - the traditional ellipse pupil-fitting algorithm and a deep learning neural network inference model. Gaze accuracy from qDVS frames achieved an average of 93.2% for movement along the primary horizontal axis (pitch angle) and 93.1 % for movement along the primary vertical axis (yaw angle) under 4 different illumination conditions, demonstrating the feasibility for using qDVS hardware cameras for such applications. The quantitative framework for the direct comparison of eye tracking algorithms presented here is made open-source and can be extended to include other eye parameters, such as pupil dilation, reflection, motion artifact, and more.

Using Eye-tracking tools in the visual assessment of architecture

Using Eye-tracking tools in the visual assessment of architecture

Investigating visual expertise in sculpture: A methodological approach using eye tracking.

Research on visual expertise has progressed significantly due to the availability of eye tracking tools. However, attempts to bring together research on expertise and eye tracking methodology provoke several challenges, because visual information processes should be studied in authentic and domain-specific environments. Among the barriers to designing appropriate research are the proper definition of levels of expertise, the tension between internal (experimental control) and external (authentic environments) validity, and the appropriate methodology to study eye movements in a three-dimensional environment. This exploratory study aims to address these challenges and to provide an adequate research setting by investigating visual expertise in sculpting. Eye movements and gaze patterns of 20 participants were investigated while looking at two sculptures in a museum. The participants were assigned to four different groups based on their level of expertise (laypersons, novices, semi-experts, experts). Using mobile eye tracking, the following parameters were measured: number of fixations, duration of fixation, dwell time in relevant areas, and revisits in relevant areas. Moreover, scan paths were analysed using the eyenalysis approach. Conclusions are drawn on both the nature of visual expertise in sculpting and the potential (and limitations) of empirical designs that aim to investigate expertise in authentic environments.

P.028 Eye movement biomarkers for early detection of multiple sclerosis disease progression

Background: There is growing body of evidence linking abnormal eye movements in people with multiple sclerosis (MS) to disease severity and cognition which could better detect disease progression. The objective of this study is to determine if a novel eye-tracking tool can accurately predict disease severity and cognitive status based on eye movement metrics and characterize how they evolve with progression. Methods: Persons with MS (n=132) will be followed over 4 years with clinical assessments every 6 months. Eye movements are also assessed while performing oculomotor tasks using Innodem Neurosciences’ patented eye-tracking technology. The eye movement metrics will be inputted into machine learning classifying algorithms to identify which metrics can serve as reliable Eye Movement Biomarkers (EMB) for MS progression and cognitive status. Results: There were 16 participants recruited as of January 2022 with mean age 47. 3 (SD 10.4; range 26-67), gender (12F/4M), EDSS 2.59 (SD 1.49; range 1.5-6.5), SDMT 51.4 (SD 14.1, range 24-78). With current enrollment, there is a negative correlation between EDSS and SDMT (r = -0.47) as observed in the literature. Conclusions: This trial will demonstrate the utility of EMBs for monitoring MS progression by improving physicians’ access to a reliable, non-invasive, sensitive and accessible marker of disease progression.

Eye-Tracking Beyond Peripersonal Space in Virtual Reality: Validation and Best Practices

Recent developments in commercial virtual reality (VR) hardware with embedded eye-tracking create tremendous opportunities for human subjects researchers. Accessible eye-tracking in VR opens new opportunities for highly controlled experimental setups in which participants can engage novel 3D digital environments. However, because VR embedded eye-tracking differs from the majority of historical eye-tracking research, in both providing for relatively unconstrained movement and stimulus presentation distances, there is a need for greater discussion around methods for implementation and validation of VR based eye-tracking tools. The aim of this paper is to provide a practical introduction to the challenges of, and methods for, 3D gaze-tracking in VR with a focus on best practices for results validation and reporting. Specifically, first, we identify and define challenges and methods for collecting and analyzing 3D eye-tracking data in VR. Then, we introduce a validation pilot study with a focus on factors related to 3D gaze tracking. The pilot study provides both a reference data point for a common commercial hardware/software platform (HTC Vive Pro Eye) and illustrates the proposed methods. One outcome of this study was the observation that accuracy and precision of collected data may depend on stimulus distance, which has consequences for studies where stimuli is presented on varying distances. We also conclude that vergence is a potentially problematic basis for estimating gaze depth in VR and should be used with caution as the field move towards a more established method for 3D eye-tracking.

An optimized Kernel Extreme Learning Machine for the classification of the autism spectrum disorder by using gaze tracking images

Autism spectrum disorder (ASD) is a lifelong neurological condition that affects how a person interacts and learns. The early and accurate diagnosis of ASD is vital to developing a comprehensive rehabilitation plan that improves the quality of life and the integration of the ASD person in the social, family, and work environment. However, the accurate diagnosis of ASD is usually affected since it is linked to the judgment of an expert, which produces biases related to the lack of objectivity. In consequence, several works have been dedicated to developing early detection techniques for ASD based on Machine Learning (ML) technologies and eye-tracking tools. The present work aims to introduce a new methodology for ASD classification, which uses Kernel Extreme Learning Machine (KELM), an objective dataset based on gaze tracking, feature extraction techniques, and data augmentation for training the model. In turn, to enhance the accuracy in ASD classification, the KELM model is optimized through the Giza Pyramids Construction (GPC) algorithm. The proposed approach includes pipeline data augmentation, dimensionality reduction, and a posterior normalization to classify ASD subjects accurately. Statistical tests and analyses were performed to validate the performance of the proposed methodology, resulting in an average accuracy of 98.8% in ASD classification.

The Information Search Habits Investigated by an Eye Tracking Technique: How to Manage the Documentary Heritage of the National Library of Latvia

Abstract The aim of the large-scale research entitled “Exploring Documentary Heritage for Building Synergies between Research and Society” is to investigate previously unexplored aspects of the documentary heritage, including the part of the legacy that has previously been subject to censorship and has therefore not been studied in depth. In order for these layers and stories to reach the public, it should initially be informed of the existence of such information, which, in turn, could create awareness and encourage action. As part of the large-scale research, the present article, using an eye tracking tool, reveals how target groups search for information on the Internet. The usability test indicates that respondents do not go to a specific information resource to search for specific information, but search is primarily started using one of the Internet search engines. The main conclusion is that, in order to ensure the availability of reliable high-quality information to the public, which is one of the key conditions for sustainable development, databases of the National Library of Latvia (NLL) should be designed in such a way that Google search engine can easily index the information contained in them.

The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review

Early and objective autism spectrum disorder (ASD) assessment, as well as early intervention are particularly important and may have long term benefits in the lives of ASD people. ASD assessment relies on subjective rather on objective criteria, whereas advances in research point to up-to-date procedures for early ASD assessment comprising eye-tracking technology, machine learning, as well as other assessment tools. This systematic review, the first to our knowledge of its kind, provides a comprehensive discussion of 30 studies irrespective of the stimuli/tasks and dataset used, the algorithms applied, the eye-tracking tools utilised and their goals. Evidence indicates that the combination of machine learning and eye-tracking technology could be considered a promising tool in autism research regarding early and objective diagnosis. Limitations and suggestions for future research are also presented.

Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis.

The core symptoms of autism spectrum disorder (ASD) mainly relate to social communication and interactions. ASD assessment involves expert observations in neutral settings, which introduces limitations and biases related to lack of objectivity and does not capture performance in real-world settings. To overcome these limitations, advances in technologies (e.g., virtual reality) and sensors (e.g., eye-tracking tools) have been used to create realistic simulated environments and track eye movements, enriching assessments with more objective data than can be obtained via traditional measures. This study aimed to distinguish between autistic and typically developing children using visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to and extraction of socially relevant information. The 55 children participated. Autistic children presented a higher number of frames, both overall and per scenario, and showed higher visual preferences for adults over children, as well as specific preferences for adults' rather than children's faces on which looked more at bodies. A set of multivariate supervised machine learning models were developed using recursive feature selection to recognize ASD based on extracted eye gaze features. The models achieved up to 86% accuracy (sensitivity=91%) in recognizing autistic children. Our results should be taken as preliminary due to the relatively small sample size and the lack of an external replication dataset. However, to our knowledge, this constitutes a first proof of concept in the combined use of virtual reality, eye-tracking tools, and machine learning for ASD recognition. LAY SUMMARY: Core symptoms in children with ASD involve social communication and interaction. ASD assessment includes expert observations in neutral settings, which show limitations and biases related to lack of objectivity and do not capture performance in real settings. To overcome these limitations, this work aimed to distinguish between autistic and typically developing children in visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to, and extraction of, socially relevant information.