Predictive Model for Dyslexia from Eye Fixation Events

A, Jothi Prabha; Bhargavi, R.; Harish, B. S.

doi:10.35940/ijeat.a1045.1291s319

Cited by 8 publications

(2 citation statements)

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“…The results were an accuracy of 95.6%, showing the methodology as quite effective. Prabha et al in [ 34 ] also analyzed the dataset obtained in [ 33 ], focusing on only the features extracted from fixations. The authors implanted an RFE feature selection algorithm and several different machine learning (ML) algorithms, including SVM, k-nearest neighbors (KNN), and random forest (RF).…”

Section: Introductionmentioning

confidence: 99%

Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%

Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features

et al. 2023

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“…But this number is also not considered as a true representation. It is said that around 16% of the worldwide population has Dyslexia [2]. The number of Dyslexics in India is roughly 15% among school going children.…”

Section: Introductionmentioning

confidence: 99%

A Study on different Classification Models for predicting Dyslexia

Chakraborty¹

2020

kjcs

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Eye tracking technology is used to record the eye positions and the movements of the eye using the optical tracking of corneal reflections. Eye tracking data collected this way can be used in a wide variety of applications like gaming, marketing, cognitive ability and psychology. One of the applications of eye tracking data is to predict whether an individual has a learning disability like Dyslexia. Dyslexia is the most common neurological learning disability which manifests in the form of difficulty in reading and spelling. Although eye tracking data is recorded and available, there is a scarcity of studies done in analysing the data and understanding the hidden relationship and classifying it appropriately. This research intends to study different classification models like Logistic Regression, Gaussian NB, SVC, Decision Tree and so on. that are applied in the prediction of risk of Dyslexia. The paper also presents the result of the accuracy of different classification models in predicting the risk of Dyslexia.

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An Effective Feature Selection and Classification Technique Based on Ensemble Learning for Dyslexia Detection

Jan

Khan

2022

Intelligent Communication Technologies and Virtual Mobile Networks

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Predictive Model for Dyslexia from Eye Fixation Events

Cited by 8 publications

References 11 publications

Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features

Accessible Dyslexia Detection with Real-Time Reading Feedback through Robust Interpretable Eye-Tracking Features

A Study on different Classification Models for predicting Dyslexia

An Effective Feature Selection and Classification Technique Based on Ensemble Learning for Dyslexia Detection

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