Serious games for a technology-enhanced early screening of handwriting difficulties

Early screening of handwriting difficulties is key to start remediation activities that help distinguishing between a simple delay and dysgraphia. Technology is fundamental in this process, as also claimed by guidelines for dysgraphia diagnosis: it allows to implement artificial intelligence techniques to help in the discrimination of the difficulty. To this end, a serious game was leveraged to assess handwriting laws altered in dysgraphia starting from symbols drawing. 66 first and second graders were longitudinally tested both with the serious game and with a handwriting proficiency test. Objective features computed from the game were tested to understand if they significantly differed between children at risk and not at risk of dysgraphia, according to a standardized clinical test used to assess handwriting. Then, machine learning models were leveraged to predict the risk and understand the areas of difficulty. On average, 62% of the features significantly differ between risk levels for first graders, whilst only 35% for second graders, thus revealing a better sensitivity in younger children. This is encouraging for an early observation. As for machine learning, a Logistic classifier was able to predict risk with an area under the precision-recall curve of 0.84 for the risk class and 0.98 for the non-risk class. The results of this study could be a valid help for an artificial intelligence-enhanced screening of dysgraphia.