No abstract

Parkinson's disease (PD) is a neurodegenerative disorder that affects a person's movement. As the early diagnosis of the disease is crucial, the main aim of this work is to implement an online analysis system of patients' handwriting, through computer vision and signal processing techniques, using the database collected in the neurology department of the University Hospital Center Hassan II in Fez. For this, we studied the handwriting tests on a WACOM graphic tablet to retrieve the spatiotemporal data (position, pressure and angles of inclination), for each point (P(n)) of the trajectory. The features vector was obtained basing on five types of features: (a) Kinematic features related to the dynamics of spiral design, (b) Mechanical based on the pressure exerted on the writing surface, (c) Inclination angles, (d) Spatial interrelation feature and (e) Pen-Up. The used classification and clustering algorithms are respectively the Hoeffding tree and the FarthestFirst clusters. We observed coherence between the classification results and the clustering ones, thus the results being encouraging and promising with a recognition rate of 98.36%