想搞非接触心率监测的可以看看，它把RGB和射频信号融合，肤色偏见和光照干扰都压下去了，EquiPleth上误差不到1 bpm。
CardiacMamba是一个融合RGB面部视频与射频（RF）心脏运动信号的rPPG框架，通过状态空间建模提升心率估计的公平性与鲁棒性。其引入时间差分Mamba模块（TDMM）增强RF信号细微变化，双向SSM交互机制对齐异构模态，通道级FFT（CFFT）优化频谱特征。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，将浅深肤色MAE差距降至0.26 bpm，并在RGB退化或RF缺失时保持稳健。
CardiacMamba: Fair and Robust RGB-RF Fusion for Remote Heart Rate Estimation via State Space Modeling
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