{"ID":2828354,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.14093","arxiv_id":"2512.14093","title":"Quality-Aware Framework for Video-Derived Respiratory Signals","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.","short_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 photoplethysmo...","url_abs":"https://arxiv.org/abs/2512.14093","url_pdf":"https://arxiv.org/pdf/2512.14093v1","authors":"[\"Nhi Nguyen\",\"Constantino Álvarez Casado\",\"Le Nguyen\",\"Manuel Lage Cañellas\",\"Miguel Bordallo López\"]","published":"2025-12-16T05:04:24Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"eess.SP\"]","methods":"[]","has_code":false}