Multimodal Ensemble with Conditional Feature Fusion for Dysgraphia Diagnosis in Children from Handwriting Samples

TL;DR

This paper introduces a novel multimodal machine learning approach with conditional feature fusion for diagnosing developmental dysgraphia in children, leveraging both online and offline handwriting data to improve accuracy and practicality.

Contribution

It proposes a new ensemble method with conditional feature fusion that intelligently combines online and offline handwriting data for better dysgraphia diagnosis.

Findings

Achieved 88.8% accuracy, outperforming single modality classifiers by 12-14%.

Demonstrated the effectiveness of conditional feature fusion over traditional methods.

Validated the approach on a newly created dataset with online and offline handwriting samples.

Abstract

Developmental dysgraphia is a neurological disorder that hinders children's writing skills. In recent years, researchers have increasingly explored machine learning methods to support the diagnosis of dysgraphia based on offline and online handwriting. In most previous studies, the two types of handwriting have been analysed separately, which does not necessarily lead to promising results. In this way, the relationship between online and offline data cannot be explored. To address this limitation, we propose a novel multimodal machine learning approach utilizing both online and offline handwriting data. We created a new dataset by transforming an existing online handwritten dataset, generating corresponding offline handwriting images. We considered only different types of word data (simple word, pseudoword & difficult word) in our multimodal analysis. We trained SVM and XGBoost classifiers separately on online and offline features as well as implemented multimodal feature fusion and soft-voted ensemble. Furthermore, we proposed a novel ensemble with conditional feature fusion method which intelligently combines predictions from online and offline classifiers, selectively incorporating feature fusion when confidence scores fall below a threshold. Our novel approach achieves an accuracy of 88.8%, outperforming SVMs for single modalities by 12-14%, existing methods by 8-9%, and traditional multimodal approaches (soft-vote ensemble and feature fusion) by 3% and 5%, respectively. Our methodology contributes to the development of accurate and efficient dysgraphia diagnosis tools, requiring only a single instance of multimodal word/pseudoword data to determine the handwriting impairment. This work highlights the potential of multimodal learning in enhancing dysgraphia diagnosis, paving the way for accessible and practical diagnostic tools.