ETDD70: Eye-Tracking Dataset for Classification of Dyslexia using AI-based Methods
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Proceedings |
| Conference | 17th International Conference on Similarity Search and Applications (SISAP) |
| MU Faculty or unit | |
| Citation | |
| web | |
| 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 |
| 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. |
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