Unsupervised learning for characterization of Arabic online handwriting of Parkinson's disease patients

In this paper, we propose to characterize the on-line handwriting for the early detection of Parkinson's disease. Thus, using kinematics, mechanical, and spatial features of handwriting, we are looking for the characterization of Parkinson's disease. This paper describes the phase of the data acquisition which is currently carried out with in the Neurological department of UHC Hassan II of Fez. Following this paper, we have proposed an approach based on unsupervised learning techniques for analyzing on-line handwriting of 34 Parkinson's disease patients and 34 Healthy Controls according to quantitative and qualitative features. Based on 230 computed features for each participant, our study has uncovered three different types of writers. The results show that the complications of fine motor abilities in Parkinson's disease patients is especially characterized by a significant degradation in handwriting kinematic features.