  One of the symptoms of Parkinson's disease (PD) is hypomimia or reduced
facial expressions. In this paper, we present a digital biomarker for PD that
utilizes the study of micro-expressions. We analyzed the facial action units
(AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, mean
age 63.9 yo, sd 7.8 ) collected online using a web-based tool
(www.parktest.net). In these videos, participants were asked to make three
facial expressions (a smiling, disgusted, and surprised face) followed by a
neutral face. Using techniques from computer vision and machine learning, we
objectively measured the variance of the facial muscle movements and used it to
distinguish between individuals with and without PD. The prediction accuracy
using the facial micro-expressions was comparable to those methodologies that
utilize motor symptoms. Logistic regression analysis revealed that participants
with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and
AU4 (brow lowerer) than non-PD individuals. An automated classifier using
Support Vector Machine was trained on the variances and achieved 95.6%
accuracy. Using facial expressions as a biomarker for PD could be potentially
transformative for patients in need of physical separation (e.g., due to COVID)
or are immobile.
