  Computer aided diagnosis systems can provide non-invasive, low-cost tools to
support clinicians. These systems have the potential to assist the diagnosis
and monitoring of neurodegenerative disorders, in particular Parkinson's
disease (PD). Handwriting plays a special role in the context of PD assessment.
In this paper, the discriminating power of "dynamically enhanced" static images
of handwriting is investigated. The enhanced images are synthetically generated
by exploiting simultaneously the static and dynamic properties of handwriting.
Specifically, we propose a static representation that embeds dynamic
information based on: (i) drawing the points of the samples, instead of linking
them, so as to retain temporal/velocity information; and (ii) adding pen-ups
for the same purpose. To evaluate the effectiveness of the new handwriting
representation, a fair comparison between this approach and state-of-the-art
methods based on static and dynamic handwriting is conducted on the same
dataset, i.e. PaHaW. The classification workflow employs transfer learning to
extract meaningful features from multiple representations of the input data. An
ensemble of different classifiers is used to achieve the final predictions.
Dynamically enhanced static handwriting is able to outperform the results
obtained by using static and dynamic handwriting separately.
