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

D 2024

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

SEDMIDUBSKÝ, Jan, Nicol DOSTÁLOVÁ, Roman ŠVAŘÍČEK and Wolf CULEMANN

Basic information

Original name

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

Authors

SEDMIDUBSKÝ, Jan (203 Czech Republic, guarantor, belonging to the institution), Nicol DOSTÁLOVÁ (203 Czech Republic, belonging to the institution), Roman ŠVAŘÍČEK (203 Czech Republic, belonging to the institution) and Wolf CULEMANN

Edition

Cham, 17th International Conference on Similarity Search and Applications (SISAP), p. 34-48, 15 pp. 2024

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

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

Organization unit

Faculty of Informatics

ISBN

978-3-031-75822-5

DOI

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

Keywords in English

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

Abstract

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.

Links

TL05000177, research and development project

Name: Diagnostika dyslexie s využitím eye-trackingu a umělé inteligence (Acronym: DYSLEX)

Investor: Technology Agency of the Czech Republic

Citovat

SEDMIDUBSKÝ, Jan, Nicol DOSTÁLOVÁ, Roman ŠVAŘÍČEK and 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, 2024, p. 34-48. ISBN 978-3-031-75822-5. Available from: https://dx.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 = {http://dx.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://link.springer.com/chapter/10.1007/978-3-031-75823-2_3},
   year = {2024}
}

TY  - JOUR
ID  - 2428459
AU  - Sedmidubský, Jan - Dostálová, Nicol - Švaříček, Roman - Culemann, Wolf
PY  - 2024
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://link.springer.com/chapter/10.1007/978-3-031-75823-2_3
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 and 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, 2024, p.~34-48. ISBN~978-3-031-75822-5. Available from: https://dx.doi.org/10.1007/978-3-031-75823-2\_{}3.

Detailed Information on Publication Record