Single-camera motion capture of finger joint mobility as a digital biomarker for disease activity in rheumatoid arthritis.

ABSTRACT:

Objective

To investigate the association between hand motion tracking features obtained through computer vision from smartphone cameras and disease activity in patients with RA.

Methods

The PyPI package of MediaPipe (version 0.9.0.1) was used for key landmark detection. Finger joint angles were calculated in each frame using the normalized dot product of the vectors (equations). RA patients were instructed to perform a rapid repetition of five fist closures. Hand movements were captured using standard smartphone cameras. Kinetic features time to maximum flexion for MCP, PIP and DIP joints were correlated with RA disease activity and disability outcomes. Logistic regression was used to investigate associations of range of motion and kinetic features with 28-joint DAS (DAS28)

SUBMITTER: Venerito V 

PROVIDER: S-EPMC12007596 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature