融合视觉与射频信号，提升远距离心率监测的准确性与鲁棒性
CardiacMamba: Fair and Robust RGB-RF Fusion for Remote Heart Rate Estimation via State Space Modeling
- 通过状态空间模型融合面部光学与射频心脏运动信号
- 在EquiPleth数据集上达到0.96 bpm MAE，光照变化下肤色差异影响仅0.26 bpm
- 适用于光照不稳或信号缺失场景，适合医疗健康监测应用
远程光体积描记（rPPG）可通过面部视频实现非接触式心率监测，但仅依赖RGB的方法易受光照变化、运动伪影和肤色相关反射率的影响。本文提出CardiacMamba，一种基于状态空间建模的公平且鲁棒的RGB-RF融合框架，结合面部光学特征与射频心脏运动信号。该方法引入时序差分Mamba模块以增强微弱的射频时序变化，设计双向状态空间交互机制对齐异构的RGB-RF动态特性，并采用通道快速傅里叶变换模块进行通道域频谱优化。在EquiPleth数据集上，CardiacMamba取得0.96 bpm MAE、3.06 bpm RMSE和0.97皮尔逊相关系数的先进性能，将光照明暗条件下肤色差异导致的MAE差距缩小至0.26 bpm，且在RGB退化和射频缺失情况下仍保持鲁棒性。
原文摘要 · Abstract (English)
Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) monitoring from facial videos, but RGB-only methods are vulnerable to illumination changes, motion artifacts, and skin-tone-dependent optical reflectance. We propose CardiacMamba, a fair and robust RGB-RF fusion framework that integrates optical facial cues and radio-frequency cardiac motion cues through state space modeling. CardiacMamba introduces a Temporal Difference Mamba Module (TDMM) to enhance subtle RF temporal variations, a bidirectional SSM-based interaction mechanism to align heterogeneous RGB-RF dynamics, and a Channel-wise Fast Fourier Transform (CFFT) module for channel-domain spectral refinement. On the EquiPleth dataset, CardiacMamba achieves state-of-the-art performance with 0.96 bpm MAE, 3.06 bpm RMSE, and 0.97 Pearson correlation, while reducing the observed light-dark skin-tone MAE gap to 0.26 bpm and maintaining robustness under RGB degradation and RF-missing conditions
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