Automated dysgraphia detection by deep learning with SensoGrip
This work addresses early detection of dysgraphia in children, offering a more automated and realistic assessment tool, though it is incremental by extending existing methods with new hardware and scoring.
The paper tackled dysgraphia detection by predicting fine-grained handwriting scores (SEMS, 0-12) using deep learning with a smart pen, achieving over 99% accuracy and RMSE below one.
Dysgraphia, a handwriting learning disability, has a serious negative impact on children's academic results, daily life and overall wellbeing. Early detection of dysgraphia allows for an early start of a targeted intervention. Several studies have investigated dysgraphia detection by machine learning algorithms using a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated fine grading of handwriting capabilities by predicting SEMS score (between 0 and 12) with deep learning. Our approach provide accuracy more than 99% and root mean square error lower than one, with automatic instead of manual feature extraction and selection. Furthermore, we used smart pen called SensoGrip, a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.