pith. sign in

arxiv: 2410.15851 · v2 · pith:5NHYQQ5Znew · submitted 2024-10-21 · 📡 eess.IV · cs.CV· cs.HC· cs.LG

R2I-rPPG: A Robust Region of Interest Selection Method for Remote Photoplethysmography to Extract Heart Rate

classification 📡 eess.IV cs.CVcs.HCcs.LG
keywords rppgmethodselectionwhenangleappliedclinicaldemonstrate
0
0 comments X
read the original abstract

The COVID-19 pandemic has underscored the need for low-cost, scalable approaches to measuring contactless vital signs, either during initial triage at a healthcare facility or virtual telemedicine visits. Remote photoplethysmography (rPPG) can accurately estimate heart rate (HR) when applied to close-up videos of healthy volunteers in well-lit laboratory settings. However, results from such highly optimized laboratory studies may not be readily translated to healthcare settings. One significant barrier to the practical application of rPPG in health care is the accurate localization of the region of interest (ROI). Clinical or telemedicine visits may involve sub-optimal lighting, movement artifacts, variable camera angle, and subject distance. This paper presents an rPPG ROI selection method based on 3D facial landmarks and patient head yaw angle. We then demonstrate the robustness of this ROI selection method when coupled to the Plane-Orthogonal-to-Skin (POS) rPPG method when applied to videos of patients presenting to an Emergency Department for respiratory complaints. Our results demonstrate the effectiveness of our proposed approach in improving the accuracy and robustness of rPPG in a challenging clinical environment.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Signal Extraction Approach for Remote Heart Rate Variability Assessment Using Proxy Measure in a Driving Simulator

    eess.IV 2026-05 unverdicted novelty 4.0

    rPPG algorithms with Lp-norm and fractional-order peak enhancement achieve 1.92 bpm MAE for pulse rate and good HRV metrics vs ECG in 29 drivers, recommending 2SR for rate and CHROM for variability with 20 superpixels.