Lomurno Eugenio, Dui Linda Greta, Gatto Madhurii, Bollettino Matteo, Matteucci Matteo, Ferrante Simona
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.
Life (Basel). 2023 Feb 21;13(3):598. doi: 10.3390/life13030598.
Dysgraphia is a neurodevelopmental disorder specific to handwriting. Classical diagnosis is based on the evaluation of speed and quality of the final handwritten text: it is therefore delayed as it is conducted only when handwriting is mastered, in addition to being highly language-dependent and not always easily accessible. This work presents a solution able to anticipate dysgraphia screening when handwriting has not been learned yet, in order to prevent negative consequences on the individuals' academic and daily life. To quantitatively measure handwriting-related characteristics and monitor their evolution over time, we leveraged the Play-Draw-Write iPad application to collect data produced by children from the last year of kindergarten through the second year of elementary school. We developed a meta-model based on deep learning techniques (ensemble techniques and Quasi-SVM) which receives as input raw signals collected after a processing phase based on dimensionality reduction techniques (autoencoder and Time2Vec) and mathematical tools for high-level feature extraction (Procrustes Analysis). The final dysgraphia classifier can identify "at-risk" children with 84.62% Accuracy and 100% Precision more than two years earlier than current diagnostic techniques.
书写障碍是一种特定于手写的神经发育障碍。传统诊断基于对最终手写文本的速度和质量的评估：因此，这种诊断具有滞后性，因为它仅在手写技能掌握之后才进行，此外还高度依赖语言且并非总是易于实施。这项工作提出了一种解决方案，能够在尚未学会手写时就提前进行书写障碍筛查，以防止对个人学业和日常生活产生负面影响。为了定量测量与手写相关的特征并监测其随时间的演变，我们利用了“玩-画-写”iPad应用程序来收集从幼儿园最后一年到小学二年级的儿童所产生的数据。我们基于深度学习技术（集成技术和拟支持向量机）开发了一个元模型，该模型接收在基于降维技术（自动编码器和时间向量化）和用于高级特征提取的数学工具（普罗克汝斯分析）的处理阶段之后收集的原始信号作为输入。最终的书写障碍分类器能够比当前诊断技术提前两年多识别出“有风险”的儿童，准确率为84.62%，精确率为100%。
