ASSESSMENT OF HANDWRITING IN PATIENTS WITH PARKINSON'S DISEASE USING NON-INTRUSIVE TASKS

This study presents two approaches for modeling the handwriting of Parkinson's Disease (PD) patients and Healthy Control (HC) subjects. One approach is based on digit-embeddings generated from a CNN architecture pre-trained with information from the MNIST corpus. The second approach consists of the computation of statistical functionals of dynamics signal collected with the digital tablet, namely azimuth, pressure, altitude, and vertical distance. The experiments are based on writing the ten digits (from 0 to 9), which is a task commonly performed in daily life activities, making this approach closer to a non-intrusive evaluation. According to the results, the accuracy of the classification between PD patients vs. HC improved from 71.8% to 74.5% when information from images is combined with the functionals of the vertical distance signal.