用眼动追踪+机器学习，88.58%准确率无创识别读写障碍
Developing a Dyslexia Indicator Using Eye Tracking
- 通过分析注视时长和眼跳异常，提取眼动特征用于诊断
- 随机森林模型达到88.58%准确率，可区分不同严重程度
- 适合教育筛查与临床辅助诊断，无需侵入性检测
读写障碍影响全球10%至20%人口，严重阻碍学习能力，亟需创新且易获取的诊断方法。本文探究将眼动追踪技术与机器学习算法结合，作为低成本的早期筛查替代方案。通过分析注视时间延长、眼跳不规则等一般眼动模式，提出改进的眼动特征提取方法。采用随机森林分类器进行诊断，准确率达88.58%。此外，应用层次聚类法识别读写障碍的不同严重程度。研究涵盖多种人群与场景，证明该技术可无创识别读写障碍个体，包括边缘状态者。眼动追踪与机器学习融合显著提升诊断效率，为临床研究提供高精度、易获取的新途径。
原文摘要 · Abstract (English)
Dyslexia, affecting an estimated 10% to 20% of the global population, significantly impairs learning capabilities, highlighting the need for innovative and accessible diagnostic methods. This paper investigates the effectiveness of eye-tracking technology combined with machine learning algorithms as a cost-effective alternative for early dyslexia detection. By analyzing general eye movement patterns, including prolonged fixation durations and erratic saccades, we proposed an enhanced solution for determining eye-tracking-based dyslexia features. A Random Forest Classifier was then employed to detect dyslexia, achieving an accuracy of 88.58\%. Additionally, hierarchical clustering methods were applied to identify varying severity levels of dyslexia. The analysis incorporates diverse methodologies across various populations and settings, demonstrating the potential of this technology to identify individuals with dyslexia, including those with borderline traits, through non-invasive means. Integrating eye-tracking with machine learning represents a significant advancement in the diagnostic process, offering a highly accurate and accessible method in clinical research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。