Facial expressions can detect Parkinson's disease: preliminary evidence from videos collected online

TL;DR

This study demonstrates that analyzing facial micro-expressions from online videos can effectively detect Parkinson's disease with high accuracy, offering a non-invasive digital biomarker.

Contribution

The paper introduces a novel digital biomarker based on facial micro-expressions analyzed via computer vision and machine learning for Parkinson's disease detection.

Findings

Support Vector Machine classifier achieved 95.6% accuracy.

PD patients showed less variance in specific facial action units.

Facial micro-expressions can serve as effective biomarkers for PD.

Abstract

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.