Quality-Aware Framework for Video-Derived Respiratory Signals

TL;DR

This paper introduces a quality-aware framework that combines multiple video-derived signals and assesses their reliability to improve respiratory rate estimation accuracy across diverse datasets.

Contribution

It presents a novel predictive, quality-aware approach that integrates heterogeneous signals and dynamically assesses their reliability for enhanced respiratory monitoring.

Findings

Achieves lower RR estimation errors than individual methods on public datasets.

Demonstrates the effectiveness of quality-based adaptive signal fusion.

Shows potential for scalable video-based respiratory monitoring solutions.

Abstract

Video-based respiratory rate (RR) estimation is often unreliable due to inconsistent signal quality across extraction methods. We present a predictive, quality-aware framework that integrates heterogeneous signal sources with dynamic assessment of reliability. Ten signals are extracted from facial remote photoplethysmography (rPPG), upper-body motion, and deep learning pipelines, and analyzed using four spectral estimators: Welch's method, Multiple Signal Classification (MUSIC), Fast Fourier Transform (FFT), and peak detection. Segment-level quality indices are then used to train machine learning models that predict accuracy or select the most reliable signal. This enables adaptive signal fusion and quality-based segment filtering. Experiments on three public datasets (OMuSense-23, COHFACE, MAHNOB-HCI) show that the proposed framework achieves lower RR estimation errors than individual methods in most cases, with performance gains depending on dataset characteristics. These findings highlight the potential of quality-driven predictive modeling to deliver scalable and generalizable video-based respiratory monitoring solutions.