Vajs Ivan, Papić Tamara, Ković Vanja, Savić Andrej M, Janković Milica M
School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia.
Innovation Center, School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia.
Brain Sci. 2023 Feb 26;13(3):405. doi: 10.3390/brainsci13030405.
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.
鉴于诵读困难的普遍性及其对教育和生活质量的负面影响，开发可靠、可量化且易于获取的诵读困难诊断和跟踪指标是一个重要目标。在本研究中，我们观察了15名患有诵读困难的塞尔维亚学龄儿童和15名神经典型的塞尔维亚学龄儿童的眼动追踪数据，这些儿童阅读呈现于不同颜色配置下的文本片段。引入了两个新的眼动追踪特征，它们可量化受试者注视的空间复杂度随时间的变化，并内在地提供有关受试者阅读最困难的文本位置的信息。这些特征从原始眼动追踪数据（x、y坐标）中提取，这些原始数据是在60Hz采集的，同时也从30Hz的下采样数据中提取，以此检验这些特征与低成本或定制眼动仪的兼容性。这些特征被用作机器学习算法的输入，在60Hz时获得的最佳准确率为88.9%，在30Hz时为87.8%。这些特征还被用于分析背景/覆盖颜色对阅读质量的影响，结果表明，无论背景/覆盖颜色如何，所引入的特征都能区分诵读困难组和对照组。然而，颜色对每个受试者的影响可能不同，这意味着需要采用个性化方法才能获得最佳治疗效果。本研究在诵读困难的检测和评估方面显示出前景，因为所提出的特征可以在阅读过程中实时作为反馈来实施，并且在使用较低采样率获得的数据检测诵读困难方面显示出有效性。
