迈向客观书写障碍检测：一种用于在线手写分析的多分支深度学习方法
Towards Objective Dysgraphia Detection: A Multi-Branch Deep Learning Approach for Online Handwriting Analysis
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中文总结 AI 辅助
针对书写障碍检测，提出基于深度学习的多分支框架，该框架含两个互补分支，分别提取不同特征并融合，利用公开数据集评估表明，GAF、MOMENT和手工运动学特征融合检测效果最佳，凸显图像与信号表示互补在书写障碍检测中的潜力。
中文摘要 AI 辅助
书写障碍是学龄儿童中普遍存在的一种特定学习障碍，影响书写连贯性、质量、流畅性和易读性，通常通过基于临床医生观察的主观评估来诊断，既耗时又易变。本文介绍了一种基于深度学习的框架，用于使用通过数字化平板电脑捕获的在线手写数据进行客观书写障碍检测。该框架依赖两个互补分支：第一个管道直接从原始时间信号中提取手工制作和基于嵌入的运动学特征，第二个利用基于连续小波变换（CWT）和格拉姆角场（GAF）生成的时间信号的基于图像的表示。然后融合得到的特征以利用两种表示的互补优势。使用公开可用的DiaGraMo数据集对四种表示进行了单独和联合评估，结果表明GAF、MOMENT和手工制作的运动学特征的融合优于每种单独的表示以及其他融合方案。这些发现突出了基于图像和信号的表示互补性在更客观的书写障碍检测中的潜力。
英文摘要
Dysgraphia is a specific learning disability that is prevalent among school-age children. It affects handwriting coherence, quality, fluency, and legibility, often hindering academic achievement and early learning development. This motor coordination disorder is typically diagnosed through subjective assessments based on clinician observation, which can be timeconsuming and prone to variability. In this paper, we introduce a deep learning-based framework for objective dysgraphia detection using online handwriting data captured via digitizing tablets. The proposed framework relies on two complementary branches: the first pipeline extracts both handcrafted and embedding-based kinematic features directly from raw temporal signals, while the second leverages image-based representations of the temporal signals generated using continuous wavelet transforms (CWT) and Gramian Angular Fields (GAF). The resulting features are then fused to leverage the complementary strengths of both representations. The four representations were evaluated separately and jointly using the publicly available DiaGraMo dataset, showing that the fusion of GAF, MOMENT, and hand-crafted kinematic features outperforms each individual representation, as well as other fusion schemes. These findings highlight the potential of the complementarity of image and signal based representations for more objective dysgraphia detection.
