HIGHLIGHTS
- who: Peter Drotu00e1r from the DepartmentTechnical University have published the research work: Dysgraphia detection through machine learning, in the Journal: Scientific Reports Scientific Reports of 22/01/2020
- what: In this study, a template for the acquisition of handwriting data was proposed and used as a source for the automated diagnosis of dysgraphia. The authors focus solely on the spatiotemporal and kinematic features since these represent the gold standard of handwriting features and are frequently used to evaluate u00adhandwriting17. This study provides new data and insights on automatic testing of dysgraphia and confirms that machine_learning . . .
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