  In this paper, we evaluate the contribution of different handwriting
modalities to the diagnosis of Parkinson's disease. We analyse on-surface
movement, in-air movement and pressure exerted on the tablet surface.
Especially in-air movement and pressure-based features have been rarely taken
into account in previous studies. We show that pressure and in-air movement
also possess information that is relevant for the diagnosis of Parkinson's
Disease (PD) from handwriting. In addition to the conventional kinematic and
spatio-temporal features, we present a group of the novel features based on
entropy and empirical mode decomposition of the handwriting signal. The
presented results indicate that handwriting can be used as biomarker for PD
providing classification performance around 89% area under the ROC curve (AUC)
for PD classification.
