Video-based quantification of leg agility in Parkinson’s disease and its relationship to upper extremity motor impairments

Abstract Bradykinesia is a hallmark sign of Parkinson’s disease (PD). Current clinical assessment is susceptible to inter-rater variability and only provides a single, ordinal severity score. Here, we propose a computer vision-based framework to quantify distinct motor characteristics from video recordings of the leg agility test, including slowness (bradykinesia), reduced amplitude (hypokinesia), progressive decrement (sequence effect) and irregularity (hesitation-halts). We validate our approach using a large-scale dataset of 3097 video recordings captured from 443 participants in the Personalized Parkinson Project. We demonstrated that features reflecting bradykinesia, hypokinesia and hesitation-halts differed significantly across clinical severity ratings, whereas sequence effect features showed no consistent differences. Furthermore, data-driven analysis using principal component analysis with varimax rotation revealed that two additional feature dimensions (i.e., beyond the four conventional domains) may be necessary to fully characterize motor abnormalities during the leg agility test. We also explored the relationship between finger-tapping (a distal upper limb task) and leg agility (a proximal lower extremity task). The analyses showed that upper and lower limb tasks provide differential insights into PD motor impairment, and that asymmetry between the least and most affected side was more pronounced in the upper extremities. This work highlights the ability of video-based assessment to provide objective characterization of motor impairment in PD, with the potential to support both in-clinic evaluations and at-home remote monitoring. Future work will investigate responsiveness to medication and longitudinal disease progression.