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

D 2025

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

SEDMIDUBSKÝ, Jan; Nicol DOSTÁLOVÁ; Roman ŠVAŘÍČEK a Wolf CULEMANN

Základní údaje

Originální název

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

Autoři

SEDMIDUBSKÝ, Jan; Nicol DOSTÁLOVÁ; Roman ŠVAŘÍČEK a Wolf CULEMANN

Vydání

Cham, 17th International Conference on Similarity Search and Applications (SISAP), od s. 34-48, 15 s. 2025

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

Collected dataset (ETDD70: Eye-tracking dyslexia dataset), URL

Organizační jednotka

Fakulta informatiky

ISBN

978-3-031-75822-5

DOI

https://doi.org/10.1007/978-3-031-75823-2_3

UT WoS

001422992900003

Klíčová slova anglicky

dyslexia;eye tracking;time-series data;classification;k-nearest neighbor query;multilayer perceptron;residual networks

Štítky

core_B, DISA, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 19. 3. 2025 10:13, doc. RNDr. Jan Sedmidubský, Ph.D.

Anotace

V originále

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.

Návaznosti

TL05000177, projekt VaV

Název: Diagnostika dyslexie s využitím eye-trackingu a umělé inteligence (Akronym: DYSLEX)

Investor: Technologická agentura ČR, Diagnostika dyslexie s využitím eye-trackingu a umělé inteligence

Citovat

SEDMIDUBSKÝ, Jan; Nicol DOSTÁLOVÁ; Roman ŠVAŘÍČEK a Wolf CULEMANN. ETDD70: Eye-Tracking Dataset for Classification of Dyslexia using AI-based Methods. Online. In 17th International Conference on Similarity Search and Applications (SISAP). Cham: Springer, 2025, s. 34-48. ISBN 978-3-031-75822-5. Dostupné z: https://doi.org/10.1007/978-3-031-75823-2_3.

@inproceedings{2428459,
   author = {Sedmidubský, Jan and Dostálová, Nicol and Švaříček, Roman and Culemann, Wolf},
   address = {Cham},
   booktitle = {17th International Conference on Similarity Search and Applications (SISAP)},
   doi = {https://doi.org/10.1007/978-3-031-75823-2_3},
   keywords = {dyslexia;eye tracking;time-series data;classification;k-nearest neighbor query;multilayer perceptron;residual networks},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-031-75822-5},
   pages = {34-48},
   publisher = {Springer},
   title = {ETDD70: Eye-Tracking Dataset for Classification of Dyslexia using AI-based Methods},
   url = {https://doi.org/10.5281/zenodo.13332134},
   year = {2025}
}

TY  - CONF
ID  - 2428459
AU  - Sedmidubský, Jan - Dostálová, Nicol - Švaříček, Roman - Culemann, Wolf
PY  - 2025
TI  - ETDD70: Eye-Tracking Dataset for Classification of Dyslexia using AI-based Methods
PB  - Springer
CY  - Cham
SN  - 9783031758225
KW  - dyslexia;eye tracking;time-series data;classification;k-nearest neighbor query;multilayer perceptron;residual networks
UR  - https://doi.org/10.5281/zenodo.13332134
N2  - 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.
ER  -

SEDMIDUBSKÝ, Jan; Nicol DOSTÁLOVÁ; Roman ŠVAŘÍČEK a Wolf CULEMANN. ETDD70: Eye-Tracking Dataset for Classification of Dyslexia using AI-based Methods. Online. In \textit{17th International Conference on Similarity Search and Applications (SISAP)}. Cham: Springer, 2025, s.~34-48. ISBN~978-3-031-75822-5. Dostupné z: https://doi.org/10.1007/978-3-031-75823-2\_{}3.

Přehled o publikaci