  Parkinson's disease (PD) is a neuro-degenerative disorder that affects
movement, speech, and coordination. Timely diagnosis and treatment can improve
the quality of life for PD patients. However, access to clinical diagnosis is
limited in low and middle income countries (LMICs). Therefore, development of
automated screening tools for PD can have a huge social impact, particularly in
the public health sector. In this paper, we present PULSAR, a novel method to
screen for PD from webcam-recorded videos of the finger-tapping task from the
Movement Disorder Society - Unified Parkinson's Disease Rating Scale
(MDS-UPDRS). PULSAR is trained and evaluated on data collected from 382
participants (183 self-reported as PD patients). We used an adaptive graph
convolutional neural network to dynamically learn the spatio temporal graph
edges specific to the finger-tapping task. We enhanced this idea with a multi
stream adaptive convolution model to learn features from different modalities
of data critical to detect PD, such as relative location of the finger joints,
velocity and acceleration of tapping. As the labels of the videos are
self-reported, there could be cases of undiagnosed PD in the non-PD labeled
samples. We leveraged the idea of Positive Unlabeled (PU) Learning that does
not need labeled negative data. Our experiments show clear benefit of modeling
the problem in this way. PULSAR achieved 80.95% accuracy in validation set and
a mean accuracy of 71.29% (2.49% standard deviation) in independent test,
despite being trained with limited amount of data. This is specially promising
as labeled data is scarce in health care sector. We hope PULSAR will make PD
screening more accessible to everyone. The proposed techniques could be
extended for assessment of other movement disorders, such as ataxia, and
Huntington's disease.
