Towards Objective Dysgraphia Detection: A Multi-Branch Deep Learning Approach for Online Handwriting Analysis

2026-07-10

AI Summary

This study addresses the limitations of traditional dysgraphia diagnosis—namely its subjectivity, time intensity, and inconsistency—by proposing a deep learning–based multi-branch fusion framework that leverages online handwriting data collected via digital tablets. The approach jointly models kinematic signals and image representations through an innovative integration of handcrafted kinematic features and embedded features. Temporal signals are transformed into images using Continuous Wavelet Transform (CWT) and Gramian Angular Fields (GAF), enabling complementary multimodal learning across dual signal- and image-based pathways. Experimental results on the DiaGraMo dataset demonstrate that the proposed GAF-MOMENT–kinematic feature fusion scheme significantly outperforms single-modality approaches and alternative fusion strategies, achieving higher diagnostic accuracy while enhancing objectivity in dysgraphia detection.

Abstract

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.

Problem

• dysgraphia
• objective detection
• online handwriting analysis
• learning disability
• motor coordination disorder

Innovation

• multi-branch deep learning
• online handwriting analysis
• Gramian Angular Fields
• continuous wavelet transform
• feature fusion