论文标题
阅读障碍和运动障碍预测：一种新的机器学习方法
Dyslexia and Dysgraphia prediction: A new machine learning approach
论文作者
论文摘要
学习障碍，剧障碍，阅读障碍，功能障碍等。干扰了学术成就，但在学术时期的后果也很长。人们普遍承认，世界人口的5％至10％受到这种残疾的影响。为了评估幼儿时期这种残疾，儿童必须解决一系列测试。人类专家对这些测试进行评分，并根据他们的标记来决定孩子是否需要特定的教育策略。评估可能是冗长，昂贵和情感上的痛苦。在本文中，我们研究了人工智能如何帮助自动化此评估。从标准儿童和内乐和/或不及格儿童中收集手写文本图片和录音数据集，我们将机器学习技术应用于分类，以分析诵读困难/依从性/差异读者和标准读取者/作家之间的差异，并建立模型。该模型经过分析图片和音频文件获得的简单功能的培训。我们的初步实现显示在我们使用的数据集上的性能相对较高。这表明，一旦有足够的数据，可以准确地通过非侵入性方法筛选阅读障碍和障碍的可能性。
Learning disabilities like dysgraphia, dyslexia, dyspraxia, etc. interfere with academic achievements but have also long terms consequences beyond the academic time. It is widely admitted that between 5% to 10% of the world population is subject to this kind of disabilities. For assessing such disabilities in early childhood, children have to solve a battery of tests. Human experts score these tests, and decide whether the children require specific education strategy on the basis of their marks. The assessment can be lengthy, costly and emotionally painful. In this paper, we investigate how Artificial Intelligence can help in automating this assessment. Gathering a dataset of handwritten text pictures and audio recordings, both from standard children and from dyslexic and/or dysgraphic children, we apply machine learning techniques for classification in order to analyze the differences between dyslexic/dysgraphic and standard readers/writers and to build a model. The model is trained on simple features obtained by analysing the pictures and the audio files. Our preliminary implementation shows relatively high performances on the dataset we have used. This suggests the possibility to screen dyslexia and dysgraphia via non-invasive methods in an accurate way as soon as enough data are available.
