  Parkinson's Disease (PD) is characterized by disorders in motor function such
as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled
movements. Medication with dopaminergic medication may alleviate those motor
symptoms, however, side-effects may include uncontrolled movements, known as
dyskinesia. In this paper, an automatic PD motor-state assessment in
free-living conditions is proposed using an accelerometer in a wrist-worn
wearable sensor. In particular, an ensemble of convolutional neural networks
(CNNs) is applied to capture the large variability of daily-living activities
and overcome the dissimilarity between training and test patients due to the
inter-patient variability. In addition, class activation map (CAM), a
visualization technique for CNNs, is applied for providing an interpretation of
the results.
