Adaptive Parameter Optimization for Robust Remote Photoplethysmography

TL;DR

This paper presents PRISM, an adaptive, training-free algorithm for remote photoplethysmography that optimizes parameters in real-time, achieving state-of-the-art accuracy without training and demonstrating robustness across diverse environments.

Contribution

Introduction of PRISM, a novel online parameter adaptation method for rPPG that improves accuracy and robustness without requiring training data.

Findings

PRISM achieves MAE of 0.77 bpm on PURE and 0.66 bpm on UBFC-rPPG datasets.

PRISM attains over 97 ext{%} accuracy at a 5 bpm threshold.

Statistical analysis shows PRISM performs comparably to supervised methods ($p > 0.2$).

Abstract

Remote photoplethysmography (rPPG) enables contactless vital sign monitoring using standard RGB cameras. However, existing methods rely on fixed parameters optimized for particular lighting conditions and camera setups, limiting adaptability to diverse deployment environments. This paper introduces the Projection-based Robust Signal Mixing (PRISM) algorithm, a training-free method that jointly optimizes photometric detrending and color mixing through online parameter adaptation based on signal quality assessment. PRISM achieves state-of-the-art performance among unsupervised methods, with MAE of 0.77 bpm on PURE and 0.66 bpm on UBFC-rPPG, and accuracy of 97.3\% and 97.5\% respectively at a 5 bpm threshold. Statistical analysis confirms PRISM performs equivalently to leading supervised methods ($p > 0.2$), while maintaining real-time CPU performance without training. This validates that adaptive time series optimization significantly improves rPPG across diverse conditions.