Analyzing Head Pose in Remotely Collected Videos of People with Parkinson’s Disease

Abstract

We developed an intelligent web interface that guides users to perform several Parkinson’s disease (PD) motion assessment tests in front of their webcam. After gathering data from 329 participants (N = 199 with PD, N = 130 without PD), we developed a methodology for measuring head motion randomness based on the frequency distribution of the motion. We found PD is associated with significantly higher randomness in side-to-side head motion as measured by the variance and number of large frequency components compared to the age-matched non-PD control group (p = 0.001, d = 0.13). Additionally, in participants taking levodopa (N = 151), the most common drug to treat Parkinson’s, the degree of random side-to-side head motion was found to follow an exponential-decay activity model following the time of the last dose taken (r = −0.404, p = 6e-5). A logistic regression model for classifying PD vs. non-PD groups identified that higher frequency components are more associated with PD. Our findings could potentially be useful toward objectively quantifying differences in head motions that may be due to either PD or PD medications.