  Parkinson's disease (PD) is commonly characterized by several motor symptoms,
such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients'
fine motor control, particularly handwriting, is a powerful tool to support PD
assessment. Over the years, various dynamic attributes of handwriting, such as
pen pressure, stroke speed, in-air time, etc., which can be captured with the
help of online handwriting acquisition tools, have been evaluated for the
identification of PD. Motion events, and their associated spatio-temporal
properties captured in online handwriting, enable effective classification of
PD patients through the identification of unique sequential patterns. This
paper proposes a novel classification model based on one-dimensional
convolutions and Bidirectional Gated Recurrent Units (BiGRUs) to assess the
potential of sequential information of handwriting in identifying Parkinsonian
symptoms. One-dimensional convolutions are applied to raw sequences as well as
derived features; the resulting sequences are then fed to BiGRU layers to
achieve the final classification. The proposed method outperformed
state-of-the-art approaches on the PaHaW dataset and achieved competitive
results on the NewHandPD dataset.
