CEA, University Grenoble Alpes, Leti, 38000 Grenoble, France.
CNRS, University Grenoble Alpes, University Savoie Mont Blanc, LPNC, 38000 Grenoble, France.
Sensors (Basel). 2021 Oct 23;21(21):7026. doi: 10.3390/s21217026.
Five to ten percent of school-aged children display dysgraphia, a neuro-motor disorder that causes difficulties in handwriting, which becomes a handicap in the daily life of these children. Yet, the diagnosis of dysgraphia remains tedious, subjective and dependent to the language besides stepping in late in the schooling. We propose a pre-diagnosis tool for dysgraphia using drawings called graphomotor tests. These tests are recorded using graphical tablets. We evaluate several machine-learning models and compare them to build this tool. A database comprising 305 children from the region of Grenoble, including 43 children with dysgraphia, has been established and diagnosed by specialists using the BHK test, which is the gold standard for the diagnosis of dysgraphia in France. We performed tests of classification by extracting, correcting and selecting features from the raw data collected with the tablets and achieved a maximum accuracy of 73% with cross-validation for three models. These promising results highlight the relevance of graphomotor tests to diagnose dysgraphia earlier and more broadly.
5%至 10%的学龄儿童存在书写障碍，这是一种神经运动障碍，导致书写困难，这给这些儿童的日常生活带来了不便。然而，书写障碍的诊断仍然繁琐、主观，且依赖于语言，而且往往在学校教育后期才介入。我们提出了一种使用绘图进行书写障碍预诊断的工具，称为图运动测试。这些测试是使用图形平板电脑记录的。我们评估了几种机器学习模型，并比较了它们以构建此工具。已经建立了一个包含来自格勒诺布尔地区 305 名儿童的数据库，其中包括 43 名患有书写障碍的儿童，这些儿童由专家使用 BHK 测试进行诊断，该测试是法国书写障碍诊断的金标准。我们通过从平板电脑收集的原始数据中提取、纠正和选择特征来进行分类测试，对于三个模型，交叉验证的最大准确率达到了 73%。这些有希望的结果突出了图运动测试在更早和更广泛地诊断书写障碍方面的相关性。
