Even though the computerised assessment of developmental dysgraphia (DD) based on online handwriting processing has increasing popularity, most of the solutions are based on a setup, where a child writes on a paper fixed to a digitizing tablet that is connected to a computer. Although this approach enables the standard way of writing using an inking pen, it is difficult to be administered by children themselves. The main goal of this study is thus to explore, whether the quantitative analysis of online handwriting recorded via a display screen tablet could sufficiently support the assessment of DD as well. For the purpose of this study, we enrolled 144 children (attending the 3rd and 4th class of a primary school), whose handwriting proficiency was assessed by a special education counsellor, and who assessed themselves by the Handwriting Proficiency Screening Questionnaires for Children (HPSQ C). Using machine learning models based on a gradient-boosting algorithm, we were able to support the DD diagnosis with up to 83.6% accuracy. The HPSQ C total score was estimated with a minimum error equal to 10.34 %. Children with DD spent significantly higher time in-air, they had a higher number of pen elevations, a bigger height of on-surface strokes, a lower in-air tempo, and a higher variation in the angular velocity. Although this study shows a promising impact of DD assessment via display tablets, it also accents the fact that modelling of subjective scores is challenging and a complex and data-driven quantification of DD manifestations is needed.

翻译：尽管基于在线笔迹处理的发展性书写障碍（DD）计算机化评估日益普及，但大多数解决方案采用如下设置：儿童在固定于数字化平板（连接计算机）的纸张上书写。虽然这种方法支持使用墨水笔的标准书写方式，但难以由儿童自行操作。本研究的主要目标是探究通过显示屏平板记录的在线笔迹量化分析是否同样能有效支持DD评估。为此，我们招募了144名儿童（就读小学三至四年级），其书写能力由特殊教育顾问评估，并通过儿童书写能力筛查问卷（HPSQ C）进行自评。使用基于梯度提升算法的机器学习模型，我们能够以最高83.6%的准确率支持DD诊断。HPSQ C总分估计的最小误差为10.34%。DD儿童在空中停留时间显著更长、提笔次数更多、纸面笔画高度更大、空中书写节律更慢，且角速度变异度更高。尽管本研究显示了通过显示屏平板评估DD的良好前景，但也强调了主观分数建模具有挑战性，需要对DD表现进行复杂的数据驱动量化分析。