Abstract
Digital handwriting acquisition enables the capture of detailed temporal and kinematic signals reflecting the motor processes underlying writing behavior. While handwriting analysis has been extensively explored in clinical or adult populations, its potential for studying developmental and educational characteristics in children remains less investigated. In this work, we examine whether handwriting dynamics encode information related to student characteristics using a large-scale online dataset collected from Japanese students from elementary school to junior high school. We systematically compare three families of handwriting-derived features: basic statistical descriptors of kinematic signals, entropy-based measures of variability, and parameters obtained from the sigma-lognormal model. Although the dataset contains dense stroke-level recordings, features are aggregated at the student level to enable a controlled comparison between representations. These features are evaluated across three prediction tasks: grade prediction, gender classification, and academic performance classification, using Linear or Logistic Regression and Random Forest models under consistent experimental settings. The results show that handwriting dynamics contain measurable signals related to developmental stage and individual differences, especially for the grade prediction task. These findings highlight the potential of kinematic handwriting analysis and confirm that through their development, children's handwriting evolves toward a lognormal motor organization.
Abstract (translated)
数字手写采集使得能够捕捉到反映书写行为背后运动过程的详细时间与动力学信号。尽管在临床或成人人群中，手写分析已经被广泛研究，但它在儿童发展和教育特征研究中的潜力尚未得到充分探索。在这项工作中，我们考察了学生的特性信息是否通过手写动态编码，并使用了一套大规模在线数据集，该数据来自日本从小学到初中阶段的学生。我们系统地比较了三种类型的手写衍生特征：运动信号的基本统计描述符、基于熵的可变性度量以及从sigma-对数正态模型中获得的参数。 尽管数据集中包含密集的笔画级记录，但为了实现表示之间的可控对比，这些特征是在学生级别上进行聚合。使用一致的实验设置，在线性和逻辑回归及随机森林模型下评估了这三个预测任务：年级预测、性别分类以及学业成绩分类。 研究结果表明，手写动态中包含了与发展阶段和个体差异相关的可测量信号，特别是在年级预测任务中尤为明显。这些发现突出了运动学手写分析的潜力，并确认了通过发展过程，儿童的手写字迹向着对数正态运动组织进化这一事实。
URL
https://arxiv.org/abs/2603.11519
