2026-06-10T13:47:34.977Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:9484 2023-06-12T16:57:23Z 2

Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features 2023 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/9484 https://www.mdpi.com/2076-3425/13/3/405 V. Ivan T. Papić V. Kovic A. Savic M. Jankovic first_pagesettingsOrder Article Reprints Open AccessArticle Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features by Ivan Vajs 1,2,*,Tamara Papić 3,*ORCID,Vanja Ković 4,Andrej M. Savić 1ORCID andMilica M. Janković 1 1 School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia 2 Innovation Center, School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia 3 Faculty of Technical Sciences, University Singidunum, Danijelova 32, 11000 Belgrade, Serbia 4 Faculty of Philosophy, University of Belgrade, Čika-Ljubina 18-20, 11000 Belgrade, Serbia * Authors to whom correspondence should be addressed. Brain Sci. 2023, 13(3), 405; https://doi.org/10.3390/brainsci13030405 Received: 19 January 2023 / Revised: 8 February 2023 / Accepted: 24 February 2023 / Published: 26 February 2023 (This article belongs to the Special Issue Developmental Dyslexia: Theories and Experimental Approaches) Download Browse Figures Versions Notes Abstract Developing reliable, quantifiable, and accessible metrics for dyslexia diagnosis and tracking represents an important goal, considering the widespread nature of dyslexia and its negative impact on education and quality of life. In this study, we observe eye-tracking data from 15 dyslexic and 15 neurotypical Serbian school-age children who read text segments presented on different color configurations. Two new eye-tracking features were introduced that quantify the amount of spatial complexity of the subject’s gaze through time and inherently provide information regarding the locations in the text in which the subject struggled the most. The features were extracted from the raw eye-tracking data (x, y coordinates), from the original data gathered at 60 Hz, and from the downsampled data at 30 Hz, examining the compatibility of features with low-cost or custom-made eye-trackers. The features were used as inputs to machine learning algorithms, and the best-obtained accuracy was 88.9% for 60 Hz and 87.8% for 30 Hz. The features were also used to analyze the influence of background/overlay color on the quality of reading, and it was shown that the introduced features separate the dyslexic and control groups regardless of the background/overlay color. The colors can, however, influence each subject differently, which implies that an individualistic approach would be necessary to obtain the best therapeutic results. The performed study shows promise in dyslexia detection and evaluation, as the proposed features can be implemented in real time as feedback during reading and show effectiveness at detecting dyslexia with data obtained using a lower sampling rate. article 10.3390/brainsci13030405 13(3) 405 2076-3425 Brain Sciences