Automated human-level diagnosis of dysgraphia using a consumer tablet

• Published in: NPJ digital medicine 2018-08, Vol.1 (1), p.42-42, Article 42
• Main Authors: Asselborn, Thibault, Gargot, Thomas, Kidziński, Łukasz, Johal, Wafa, Cohen, David, Jolly, Caroline, Dillenbourg, Pierre
• Format: Article
• Language: English
• Subjects: 692/700/139; 692/700/1719; Biomedicine; Biotechnology; Cognitive science; Computer Science; Digital technology; Handwriting; Health informatics; Medical diagnosis; Medicine; Medicine & Public Health; Pediatrics; Psychology
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-018-0049-x

The academic and behavioral progress of children is associated with the timely development of reading and writing skills. Dysgraphia, characterized as a handwriting learning disability, is usually associated with dyslexia, developmental coordination disorder (dyspraxia), or attention deficit disorder, which are all neuro-developmental disorders. Dysgraphia can seriously impair children in their everyday life and require therapeutic care. Early detection of handwriting difficulties is, therefore, of great importance in pediatrics. Since the beginning of the 20th century, numerous handwriting scales have been developed to assess the quality of handwriting. However, these tests usually involve an expert investigating visually sentences written by a subject on paper, and, therefore, they are subjective, expensive, and scale poorly. Moreover, they ignore potentially important characteristics of motor control such as writing dynamics, pen pressure, or pen tilt. However, with the increasing availability of digital tablets, features to measure these ignored characteristics are now potentially available at scale and very low cost. In this work, we developed a diagnostic tool requiring only a commodity tablet. To this end, we modeled data of 298 children, including 56 with dysgraphia. Children performed the BHK test on a digital tablet covered with a sheet of paper. We extracted 53 handwriting features describing various aspects of handwriting, and used the Random Forest classifier to diagnose dysgraphia. Our method achieved 96.6% sensibility and 99.2% specificity. Given the intra-rater and inter-rater levels of agreement in the BHK test, our technique has comparable accuracy for experts and can be deployed directly as a diagnostics tool.