  There is a growing interest in using pose estimation algorithms for
video-based assessment of Bradykinesia in Parkinson's Disease (PD) to
facilitate remote disease assessment and monitoring. However, the accuracy of
pose estimation algorithms in videos from video streaming services during
Telehealth appointments has not been studied. In this study, we used seven
off-the-shelf hand pose estimation models to estimate the movement of the thumb
and index fingers in videos of the finger-tapping (FT) test recorded from
Healthy Controls (HC) and participants with PD and under two different
conditions: streaming (videos recorded during a live Zoom meeting) and
on-device (videos recorded locally with high-quality cameras). The accuracy and
reliability of the models were estimated by comparing the models' output with
manual results. Three of the seven models demonstrated good accuracy for
on-device recordings, and the accuracy decreased significantly for streaming
recordings. We observed a negative correlation between movement speed and the
model's accuracy for the streaming recordings. Additionally, we evaluated the
reliability of ten movement features related to bradykinesia extracted from
video recordings of PD patients performing the FT test. While most of the
features demonstrated excellent reliability for on-device recordings, most of
the features demonstrated poor to moderate reliability for streaming
recordings. Our findings highlight the limitations of pose estimation
algorithms when applied to video recordings obtained during Telehealth visits,
and demonstrate that on-device recordings can be used for automatic
video-assessment of bradykinesia in PD.
