Tracking data
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Current diagnostic methods for dyslexia primarily rely on traditional paper-and-pencil tasks. Advanced technological approaches, including eye-tracking and artificial intelligence (AI), offer enhanced diagnostic capabilities. In this paper, we bridge the gap between scientific and diagnostic concepts by proposing a novel dyslexia detection method, called INSIGHT, which combines a visualisation phase and a neural network-based classification phase. The first phase involves transforming eye-tracking fixation data into 2D visualisations called Fix-images, which clearly depict reading difficulties. The second phase utilises the ResNet18 convolutional neural network for classifying these images. The INSIGHT method was tested on 35 child participants (13 dyslexic and 22 control readers) using three text-reading tasks, achieving a highest accuracy of 86.65%. Additionally, we cross-tested the method on an independent dataset of Danish readers, confirming the robustness and generalizability of our approach with a notable accuracy of 86.11%. This innovative approach not only provides detailed insight into eye movement patterns when reading but also offers a robust framework for the early and accurate diagnosis of dyslexia, supporting the potential for more personalised and effective interventions.
- Klíčová slova
- AI‐based diagnosis, ResNet18, deep learning, dyslexia, eye movement, eye tracking, fixation data classification,
- MeSH
- čtení MeSH
- dítě MeSH
- dyslexie * diagnóza klasifikace patofyziologie MeSH
- konvoluční neuronové sítě MeSH
- lidé MeSH
- neuronové sítě * MeSH
- oční fixace * fyziologie MeSH
- technologie sledování pohybu očí * MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Understanding animal movement is at the core of ecology, evolution and conservation science. Big data approaches for animal tracking have facilitated impactful synthesis research on spatial biology and behavior in ecologically important and human-impacted regions. Similarly, databases of animal traits (e.g. body size, limb length, locomotion method, lifespan) have been used for a wide range of comparative questions, with emerging data being shared at the level of individuals and populations. Here, we argue that the proliferation of both types of publicly available data creates exciting opportunities to unlock new avenues of research, such as spatial planning and ecological forecasting. We assessed the feasibility of combining animal tracking and trait databases to develop and test hypotheses across geographic, temporal and biological allometric scales. We identified multiple research questions addressing performance and distribution constraints that could be answered by integrating trait and tracking data. For example, how do physiological (e.g. metabolic rates) and biomechanical traits (e.g. limb length, locomotion form) influence migration distances? We illustrate the potential of our framework with three case studies that effectively integrate trait and tracking data for comparative research. An important challenge ahead is the lack of taxonomic and spatial overlap in trait and tracking databases. We identify critical next steps for future integration of tracking and trait databases, with the most impactful being open and interlinked individual-level data. Coordinated efforts to combine trait and tracking databases will accelerate global ecological and evolutionary insights and inform conservation and management decisions in our changing world.
- Klíčová slova
- Biologging, Integration, Macroecology, Repository, Tracking data, Trait data,
- MeSH
- databáze faktografické MeSH
- ekologie * metody MeSH
- lokomoce MeSH
- migrace zvířat * MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").
- Klíčová slova
- Data quality, Eye movements, Eye tracking, Replicability, Reporting guidelines, Reporting practices, Reporting standards, Reproducibility,
- MeSH
- empirický výzkum MeSH
- lidé MeSH
- pohyby očí * MeSH
- technologie sledování pohybu očí * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- odvolaná publikace MeSH
- přehledy MeSH
- Research Support, N.I.H., Extramural MeSH
The cognitive processing of learning materials has been extensively studied within various cognitive theories. Self-regulated learning (SRL) is also recognized as a key factor in learning efficiency. However, evidence linking SRL to learning outcomes remains inconclusive, particularly regarding objective behavioral data during learning. This study presents an original empirical dataset on eye-tracking activity during learning, examining the effects of metacognitive prompts and multimedia content on cognitive processing and learning outcomes. A controlled laboratory experiment with a 2 × 2 mixed factorial design involved 110 university students, resulting in 84 complete recordings of eye-movement activity during learning. Participants studied scientific materials in text-only and multimedia formats, with one group receiving metacognitive prompts and the control group receiving general instructions. Learning performance was assessed via a post-test, and eye-tracking technology captured gaze patterns to provide insights into cognitive engagement and attention distribution. Applications extend to e-learning, virtual environments, and user interface design. While the dataset has some methodological limitations, it remains a robust resource for studying cognitive processes and optimizing educational technologies.
- MeSH
- kognice MeSH
- lidé MeSH
- mladý dospělý MeSH
- pohyby očí * MeSH
- sebekontrola * MeSH
- technologie sledování pohybu očí * MeSH
- učení * MeSH
- Check Tag
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- dataset MeSH
AIMS: This systematic review aimed to investigate whether quantitative metrics derived from gaze tracking (GT) outputs during visual field (VF) testing with an automated perimeter could enhance the evaluation of test reliability. MATERIALS AND METHODS: A systematic search of PubMed, Cochrane, LILACS, and IBECS databases, from inception to August 31, 2024, was conducted. RESULTS: Eight studies - four cross-sectional and four cohort - met the inclusion criteria, comprising 8,181 visual field tests from 3,687 patients. The studies were categorized based on testing strategy: SITA Standard, Fast, and Faster. In the SITA Standard group, GT parameters were associated with visual field result reproducibility and the structure-function relationship in glaucoma, but were influenced by ocular surface variables. In the SITA Fast and Faster group, results were mixed: some studies suggested GT metrics could complement conventional reliability parameters, while others concluded that GT quantitative metrics did not offer clinically meaningful insights beyond existing methods. CONCLUSION: GT trace quantification shows promise as an objective reliability parameter for VF testing, particularly within the SITA Standard framework. Advanced image analysis techniques, including artificial intelligence, could facilitate automated GT parameter quantification, streamlining processes and supporting further studies to evaluate their impact on VF data reliability and clinical decision-making.
- Klíčová slova
- automated perimetry, gaze tracking, glaucoma, visual field,
- MeSH
- glaukom * diagnóza patofyziologie MeSH
- lidé MeSH
- reprodukovatelnost výsledků MeSH
- technologie sledování pohybu očí * MeSH
- testy zrakového pole * metody MeSH
- zraková pole * fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- systematický přehled MeSH
Simulations and games bring the possibility to research complex processes of managerial decision-making. However, this modern field requires adequate methodological procedures. Many authors recommend the use of a combination of concurrent think-aloud (CTA) or retrospective think-aloud (RTA) with eye-tracking to investigate cognitive processes such as decision-making. Nevertheless, previous studies have little or no consideration of the possible differential impact of both think-aloud methods on data provided by eye-tracking. Therefore, the main aim of this study is to compare and assess if and how these methods differ in terms of their impact on eye-tracking. The experiment was conducted for this purpose. Participants were 14 managers who played a specific simulation game with CTA use and 17 managers who played the same game with RTA use. The results empirically prove that CTA significantly distorts data provided by eye-tracking, whereas data gathered when RTA is used, provide independent pieces of evidence about the participants' behavior. These findings suggest that RTA is more suitable for combined use with eye-tracking for the purpose of the research of decision-making in the game environment.
- Klíčová slova
- decision-making, eye-tracking, games, simulation game, think-aloud,
- MeSH
- experimentální hry * MeSH
- lidé MeSH
- rozhodování * MeSH
- technologie sledování pohybu očí * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.
We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.
- MeSH
- algoritmy * MeSH
- benchmarking MeSH
- buněčné linie MeSH
- buněčný tracking metody MeSH
- interpretace obrazu počítačem * MeSH
- lidé MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Traffic signs are an integral part of the traffic control plan and they provide road users with necessary information on the upcoming situation. This paper aims to examine the level of understanding of traffic sign imagery used in different countries and to track participants' eye movement when they encounter unfamiliar signs. Tobii eye tracking glasses were used to track gaze differences between familiar and unfamiliar traffic signs. Our findings show that sign characteristics (such as the amount of information on the sign) and the observer's knowledge of the sign meaning have a significant impact on eye behaviour. Signs containing more information (loaded with more content) and unfamiliar to the participant systematically produced the longest overall and average fixations and gazing duration. Given that longer gaze time for unfamiliar traffic signs presents a potential traffic hazard, we evaluated the need for standardization of traffic signs.
- Klíčová slova
- Cross-cultural, Eye tracking, Traffic safety, Traffic sign,
- MeSH
- časové faktory MeSH
- dospělí MeSH
- lidé MeSH
- mezinárodní spolupráce MeSH
- oční fixace fyziologie MeSH
- orientační tabule a značení normy MeSH
- pohyby očí fyziologie MeSH
- pozornost MeSH
- řízení motorových vozidel psychologie MeSH
- rozpoznávání (psychologie) fyziologie MeSH
- světelná stimulace MeSH
- technologie sledování pohybu očí MeSH
- znalosti MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
This scoping review examines the use of eye movement tracking in personality research across various domains, including job interviews, education and training, human-robot interaction, and user interface design. Eye-tracking has proven effective in capturing behavioral cues linked to personality traits such as emotional responses, leadership potential, and learning preferences. To map existing research and identify prevailing use case scenarios, a systematic search was conducted in the ACM and IEEE digital libraries. From an initial pool of 170 studies, 21 met the inclusion criteria and were subjected to full-text analysis. The purpose of this review is to provide a structured overview of current research trends, methodological approaches, and application contexts. Its contribution lies in synthesizing key insights and highlighting opportunities for future research, particularly in the use of eye-tracking for advancing personalized technologies and behavior-based analytics in fields such as education, marketing, and psychological analysis.
- Klíčová slova
- eye, personality, review, tracking, trait,
- MeSH
- lidé MeSH
- oční fixace * fyziologie MeSH
- osobnost * fyziologie MeSH
- technologie sledování pohybu očí * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- scoping review MeSH
