CardiacMamba：基于状态空间建模的用于远程心率估计的公平且鲁棒的RGB-RF融合框架
CardiacMamba: Fair and Robust RGB-RF Fusion for Remote Heart Rate Estimation via State Space Modeling
- Great Bay University（大湾区大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对仅RGB远程心率估计易受光照、运动及肤色影响的问题，提出CardiacMamba融合框架，引入TDMM等模块实现异质线索对齐，在EquiPleth数据集上达最优性能且肤色差距小、鲁棒性强。
AI中文摘要：
远程光体积描记（rPPG）可从面部视频实现非接触式心率（HR）监测，但仅基于RGB的方法易受光照变化、运动伪影及肤色依赖的光学反射影响。本文提出CardiacMamba，这是一种公平且鲁棒的RGB-RF融合框架，通过状态空间建模整合面部光学线索与射频（RF）心脏运动线索。CardiacMamba引入时间差分Mamba模块（TDMM）以增强细微RF时间变化，采用基于双向状态空间模型（SSM）的交互机制对齐异质RGB-RF动态，还设有通道域快速傅里叶变换（CFFT）模块用于通道域频谱优化。在EquiPleth数据集上，CardiacMamba实现了0.96 bpm的平均绝对误差（MAE）、3.06 bpm的均方根误差（RMSE）及0.97的皮尔逊相关系数，达到了当前最优性能，同时将观测到的深浅肤色MAE差距缩小至0.26 bpm，并在RGB退化及RF缺失条件下保持了鲁棒性。
英文摘要：
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
