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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/TL05000177" target="_blank" >TL05000177: Diagnostika dyslexie s využitím eye-trackingu a umělé inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

17th International Conference on Similarity Search and Applications (SISAP)

ISBN

9783031758225

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

15

Strana od-do

34-48

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Providence, USA

Datum konání akce

1. 1. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001422992900003