Parkinson’s disease (PD) is neurodegenerative movement disorder that causes a series of motor deficits. Due
to the degenerative nature of PD, effective management of motor symptoms requires frequent and accurate
motor assessment. Current approaches for PD motor assessment rely on subjectively rated clinical scales or
research-grade equipment that is expensive or data-limited. These limitations significantly restrict the ability to
perform frequent, granular assessments of motor function and highlight an important need for new approaches
that enable objective motor assessments in any setting with minimal costs of time, money, or effort.
Here, we propose an automated, video-based approach to PD motor assessment by using a state of the art
pose estimation algorithm. Briefly, pose estimation is an emerging technology that is capable of quantitatively
tracking human movement from simple digital videos recorded using common household devices (e.g.,
smartphones, tablets). The development of pose estimation algorithms for tracking human movement has
progressed rapidly in the artificial intelligence community, and there is now significant untapped potential for
leveraging this technology to perform rapid, automated motor assessments directly in home or clinic.
In Aim 1.1, we will test a pose estimation approach for gait assessment in persons with PD. Gait dysfunction is
common in PD, but it remains difficult to assess gait quantitatively in the home or clinic. Gait assessment is not
represented adequately in clinical scales of motor function in PD, and there are no good methods for objective
measurement of whole-body gait kinematics that can be used directly in the home or clinic. We will address
this need by using a video-based pose estimation workflow to perform gait assessments in persons with PD.
We will compare these results to motion capture measurements to examine how well the pose estimation
approach approximates ground-truth measurements.
In Aim 1.2, we will use our pose estimation approach to track repetitive movements in persons with PD. PD
often causes difficulty in performing repetitive movements (e.g., finger tapping); accordingly, assessment of
repetitive movements constitutes a significant portion of the MDS-Unified Parkinson’s Disease Rating Scale
(the standard clinical scale for motor assessment in PD). This assessment is done through subjective human
ratings; here, we will test an automated, objective, video-based approach for tracking repetitive movements in
persons with PD and again compare to ground-truth motion capture measurements.
In Aim 2, we will compare and contrast motor deficits identified by pose estimation and human assessment.
Subjective visual assessment is the most common means of assessing motor function in PD, but computer
vision approaches (e.g., pose estimation) have significant potential to automate and objectivize this process.
Here, we will compare how well deficits in gait and repetitive movements detected by our pose estimation
approach align with neurologist ratings of these tasks on the Unified Parkinson’s Disease Rating Scale.