Quality-Aware Framework for Video-Derived Respiratory Signals

• URL: http://arxiv.org/abs/2512.14093v1
• Date: Tue, 16 Dec 2025 05:04:24 GMT
• Title: Quality-Aware Framework for Video-Derived Respiratory Signals
• Authors: Nhi Nguyen, Constantino Álvarez Casado, Le Nguyen, Manuel Lage Cañellas, Miguel Bordallo López,
• Abstract summary: Video-based respiratory rate estimation is often unreliable due to inconsistent signal quality across extraction methods.<n>We present a predictive, quality-aware framework that integrates heterogeneous signal sources with dynamic assessment of reliability.
• Score: 10.760284839837395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection [51.93878677594561]
  In unsupervised graph-level OOD detection, models are typically trained using only in-distribution (ID) data.<n>We propose a Policy-Guided Outlier Synthesis framework that replaces statics with a learned exploration strategy.
  arXiv  Detail & Related papers  (2026-02-28T11:40:18Z)
• How does downsampling affect needle electromyography signals? A generalisable workflow for understanding downsampling effects on high-frequency time series [1.3648865252191944]
  Automated analysis of needle electromyography (nEMG) signals is emerging as a tool to support the detection of neuromuscular diseases.<n>Downsampling offers a potential solution, but its impact on diagnostic signal content and classification performance remains insufficiently understood.<n>This study presents a workflow for systematically evaluating information loss caused by downsampling in high-frequency time series.
  arXiv  Detail & Related papers  (2026-01-15T08:46:56Z)
• Flexible Gravitational-Wave Parameter Estimation with Transformers [73.44614054040267]
  We introduce a flexible transformer-based architecture paired with a training strategy that enables adaptation to diverse analysis settings at inference time.<n>We demonstrate that a single flexible model -- called Dingo-T1 -- can analyze 48 gravitational-wave events from the third LIGO-Virgo-KAGRA Observing Run.
  arXiv  Detail & Related papers  (2025-12-02T17:49:08Z)
• A Hybrid Deep Learning Model for Robust Biometric Authentication from Low-Frame-Rate PPG Signals [0.34376560669160394]
  Photoplethymography (volution) signals, which measure changes in blood volume in the skin using light, have recently gained attention in biometric authentication.<n>Photoplethymography signal quality is challenged by motion artifacts, illumination changes, and inter-subject physiological variability.<n>This study proposes a lightweight and cost-effective biometric authentication framework based on PPG signals extracted from low-frame-rate fingertip videos.
  arXiv  Detail & Related papers  (2025-11-06T04:16:13Z)
• Adaptive Signal Analysis for Automated Subsurface Defect Detection Using Impact Echo in Concrete Slabs [0.0]
  This pilot study presents a novel, automated, and scalable methodology for detecting subsurface defect-prone regions in concrete slabs.<n>The approach integrates advanced signal processing, clustering, and visual analytics to identify subsurface anomalies.<n>The results demonstrate the robustness of the methodology, consistently identifying defect-prone areas with minimal false positives and few missed defects.
  arXiv  Detail & Related papers  (2024-12-23T20:05:53Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy.<n>We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods.<n>By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals [37.788535094404644]
  Atrial fibrillation (AF) significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography ( PPG) signals are susceptible to corruption from motion artifacts and other factors often encountered in ambulatory settings. We propose a novel deep learning model, designed to learn how to retain accurate predictions from partially corrupted PPG.
  arXiv  Detail & Related papers  (2024-04-15T01:07:08Z)
• Sparse learned kernels for interpretable and efficient medical time series processing [19.5212988158778]
  Deep learning methods were compute-intensive and lacked interpretability. We propose Sparse Mixture of Learned Kernels (SMoLK), an interpretable architecture for medical time-series signals. SMoLK learns a set of lightweight flexible kernels that form a single-layer sparse neural network.
  arXiv  Detail & Related papers  (2023-07-06T18:11:58Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection [31.125812018296127]
  We introduce a novel approach for change detection by pre-training a Deno Diffusionising Probabilistic Model (DDPM) DDPM learns the training data distribution by gradually converting training images into a Gaussian distribution using a Markov chain. During inference (i.e., sampling), they can generate a diverse set of samples closer to the training distribution. Experiments conducted on the LEVIR-CD, WHU-CD, DSIFN-CD, and CDD datasets demonstrate that the proposed DDPM-CD method significantly outperforms the existing change detection methods in terms of F1 score, I
  arXiv  Detail & Related papers  (2022-06-23T17:58:29Z)
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
  We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
  arXiv  Detail & Related papers  (2020-11-05T11:34:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.