  This paper presents a novel deep learning enabled, video based analysis
framework for assessing the Unified Parkinsons Disease Rating Scale (UPDRS)
that can be used in the clinic or at home. We report results from comparing the
performance of the framework to that of trained clinicians on a population of
32 Parkinsons disease (PD) patients. In-person clinical assessments by trained
neurologists are used as the ground truth for training our framework and for
comparing the performance. We find that the standard sit-to-stand activity can
be used to evaluate the UPDRS sub-scores of bradykinesia (BRADY) and posture
instability and gait disorders (PIGD). For BRADY we find F1-scores of 0.75
using our framework compared to 0.50 for the video based rater clinicians,
while for PIGD we find 0.78 for the framework and 0.45 for the video based
rater clinicians. We believe our proposed framework has potential to provide
clinically acceptable end points of PD in greater granularity without imposing
burdens on patients and clinicians, which empowers a variety of use cases such
as passive tracking of PD progression in spaces such as nursing homes, in-home
self-assessment, and enhanced tele-medicine.
