通过动态评估视频信号质量，提升呼吸率估计的可靠性。
Quality-Aware Framework for Video-Derived Respiratory Signals
- 融合10种不同来源信号，用质量指数动态筛选可靠数据
- 在3个公开数据集上误差低于单一方法，性能随数据特性提升
- 适合需要高鲁棒性视频呼吸监测的应用场景
基于视频的呼吸率（RR）估计常因信号质量不一致而不可靠。本文提出一种预测性、质量感知的框架，整合异构信号源并动态评估其可靠性。从面部远距离光电容积脉搏波（rPPG）、上身运动及深度学习管道中提取10种信号，使用四种谱估计方法（Welch法、MUSIC、FFT、峰值检测）进行分析。利用分段级质量指数训练机器学习模型，以预测精度或选择最可靠信号。该方法实现自适应信号融合与基于质量的段落过滤。在三个公开数据集（OMuSense-23、COHFACE、MAHNOB-HCI）上的实验表明，所提框架在多数情况下比单一方法误差更低，性能增益取决于数据集特征。结果表明，质量驱动的预测建模具有实现可扩展、通用化视频呼吸监测的潜力。
原文摘要 · Abstract (English)
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.
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