Characterizing Disease Progression in Parkinson’s Disease from Videos of the Finger Tapping Test

Congratulations to Drs. Joshua Wong, Nikolaus McFarland and Adlofo Rameriez-Zamora on the publication of “Characterizing Disease Progression in Parkinson’s Disease from Videos of the Finger Tapping Test,” which appears in the June issue of IEE Transactions on Neural Systems.


Introduction: Parkinson’s disease (PD) is characterized by motor symptoms whose progression is typically assessed using clinical scales, namely the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Despite its reliability, the scale is bounded by a 5-point scale that limits its ability to track subtle changes in disease progression based on severity and is prone to subjective interpretations. We aimed to develop an automated system to objectively quantify motor symptoms in PD using Machine Learning (ML) algorithms to analyze videos and capture nuanced features of disease progression.

Methods: We analyzed videos of the Finger Tapping test, a component of the MDS-UPDRS, from 24 healthy controls and 66 PD patients using ML algorithms for hand pose estimation. We computed multiple movement features related to bradykinesia from videos and employed a novel tiered classification approach to predict disease severity that employed different features according to severity. We compared our video-based disease severity prediction approach against other approaches recently introduced in the literature.

Results: Traditional kinematics features such as amplitude and velocity changed linearly with disease severity, while other non-traditional features displayed non-linear trends. The proposed disease severity prediction approach demonstrated superior accuracy in detecting PD and distinguishing between different levels of disease severity when compared to existing approaches.

Conclusions: Our findings support the use of automated video analysis to quantify the severity of motor symptoms in PD. This method offers a continuous and nuanced evaluation of motor symptoms that could revolutionize PD management and assessment of treatment efficacy.