ETDD70: Eye-Tracking Dataset for Classification of Dyslexia using AI-based Methods

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00144127" target="_blank" >RIV/00216224:14330/25:00144127 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-75823-2_3" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-75823-2_3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-75823-2_3" target="_blank" >10.1007/978-3-031-75823-2_3</a>

Result language
angličtina
Original language name
ETDD70: Eye-Tracking Dataset for Classification of Dyslexia using AI-based Methods
Original language description
Dyslexia, a specific learning disorder, poses challenges in reading and language processing. Traditional diagnostic methods often rely on subjective assessments, leading to inaccuracies and delays in intervention. This work proposes classifying dyslexia using AI-based methods applied to eye-tracking data captured during text reading tasks. To facilitate future research in this domain, we collect a novel dataset (ETDD70) comprising eye-tracking recordings of 70 individuals for three reading tasks. In particular, the dataset contains high-frequency and accurate time series of 2D positions of eye movements and many derived characteristics extracted from eye movement patterns. By leveraging similarity-search approaches and deep learning models, we demonstrate the utility of such data in training several classification models, the best of which can distinguish between dyslexic and non-dyslexic individuals with an accuracy of around 90%. Both the dataset and evaluated models provide a valuable resource for researchers to further advance AI-based methods for dyslexia classification.
Czech name
—
Czech description
—

Project
<a href="/en/project/TL05000177" target="_blank" >TL05000177: Diagnostics of dyslexia using eye-tracking and artificial intelligence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
17th International Conference on Similarity Search and Applications (SISAP)
ISBN
9783031758225
ISSN
0302-9743
e-ISSN
—
Number of pages
15
Pages from-to
34-48
Publisher name
Springer
Place of publication
Cham
Event location
Providence, USA
Event date
Jan 1, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001422992900003

Similar results(10)