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Article

Spatiotemporal Eye-Tracking Feature Set for Improved Recognition of Dyslexic Reading Patterns in Children

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School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia
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Innovation Center, School of Electrical Engineering in Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia
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Faculty of Philosophy, University of Belgrade, Čika-Ljubina 18-20, 11000 Belgrade, Serbia
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Faculty of Technical Sciences, University Singidunum, Danijelova 32, 11000 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Academic Editors: Jordi Solé-Casals, César F. Caiafa, Zhe Sun, Pere Marti-Puig and Toshihisa Tanaka
Sensors 2022, 22(13), 4900; https://doi.org/10.3390/s22134900
Received: 3 June 2022 / Revised: 24 June 2022 / Accepted: 27 June 2022 / Published: 29 June 2022
(This article belongs to the Special Issue Machine Learning Methods for Biomedical Data Analysis)
Considering the detrimental effects of dyslexia on academic performance and its common occurrence, developing tools for dyslexia detection, monitoring, and treatment poses a task of significant priority. The research performed in this paper was focused on detecting and analyzing dyslexic tendencies in Serbian children based on eye-tracking measures. The group of 30 children (ages 7–13, 15 dyslexic and 15 non-dyslexic) read 13 different text segments on 13 different color configurations. For each text segment, the corresponding eye-tracking trail was recorded and then processed offline and represented by nine conventional features and five newly proposed features. The features were used for dyslexia recognition using several machine learning algorithms: logistic regression, support vector machine, k-nearest neighbor, and random forest. The highest accuracy of 94% was achieved using all the implemented features and leave-one-out subject cross-validation. Afterwards, the most important features for dyslexia detection (representing the complexity of fixation gaze) were used in a statistical analysis of the individual color effects on dyslexic tendencies within the dyslexic group. The statistical analysis has shown that the influence of color has high inter-subject variability. This paper is the first to introduce features that provide clear separability between a dyslexic and control group in the Serbian language (a language with a shallow orthographic system). Furthermore, the proposed features could be used for diagnosing and tracking dyslexia as biomarkers for objective quantification. View Full-Text
Keywords: developmental dyslexia; reading; screening; colored background; eye-tracking; feature extraction; machine learning; support vector machine; k-nearest neighbors; random forest; logistic regression developmental dyslexia; reading; screening; colored background; eye-tracking; feature extraction; machine learning; support vector machine; k-nearest neighbors; random forest; logistic regression
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MDPI and ACS Style

Vajs, I.; Ković, V.; Papić, T.; Savić, A.M.; Janković, M.M. Spatiotemporal Eye-Tracking Feature Set for Improved Recognition of Dyslexic Reading Patterns in Children. Sensors 2022, 22, 4900. https://doi.org/10.3390/s22134900

AMA Style

Vajs I, Ković V, Papić T, Savić AM, Janković MM. Spatiotemporal Eye-Tracking Feature Set for Improved Recognition of Dyslexic Reading Patterns in Children. Sensors. 2022; 22(13):4900. https://doi.org/10.3390/s22134900

Chicago/Turabian Style

Vajs, Ivan, Vanja Ković, Tamara Papić, Andrej M. Savić, and Milica M. Janković. 2022. "Spatiotemporal Eye-Tracking Feature Set for Improved Recognition of Dyslexic Reading Patterns in Children" Sensors 22, no. 13: 4900. https://doi.org/10.3390/s22134900

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