# Quality-Aware Framework for Video-Derived Respiratory Signals

arXiv: https://arxiv.org/abs/2512.14093 · HTML: https://mlsanity.com/paper/2512.14093/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer](https://mlsanity.com/paper/2111.12082/) (2021)
- [The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video](https://mlsanity.com/paper/2111.09748/) (2021)
- [rPPG-Toolbox: Deep Remote PPG Toolbox](https://mlsanity.com/paper/2210.00716/) (2022)
- [A Reproducible Study on Remote Heart Rate Measurement](https://mlsanity.com/paper/1709.00962/) (2017)
- [Contrast-Phys: Unsupervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast](https://mlsanity.com/paper/2208.04378/) (2022)
- [Face2PPG: An unsupervised pipeline for blood volume pulse extraction from faces](https://mlsanity.com/paper/2202.04101/) (2022)
- [Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation](https://mlsanity.com/paper/2508.17924/) (2025)
- [OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis](https://mlsanity.com/paper/2407.06137/) (2024)

## Built on by (most cited first, 1 of 1)

- [Oracle headroom without signal: null-calibrated evaluation of candidate selection for thermal heart rate estimation](https://mlsanity.com/paper/2610.02561/) (2026)

## Abstract (arXiv)

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.