用手机前后摄像头同步采集面部和指尖血流信号，提升心率监测准确性。
M3PD Dataset: Dual-view Photoplethysmography (PPG) Using Front-and-rear Cameras of Smartphones in Lab and Clinical Settings
- 利用手机前后摄像头双视角采集血流信号，融合多源信息增强鲁棒性。
- 在60人（含47名心血管患者）数据上，心率误差降低21.9%~30.2%。
- 首个公开双视角移动脉搏波数据集，适合临床与真实场景研究者使用。
便携式生理监测对心血管疾病早期发现与管理至关重要，但现有方法常需专用设备或要求患者维持不自然体位，限制可及性。基于智能手机的视频光电容积脉搏波描记法（PPG）提供了便捷的无创替代方案，但仍受运动伪影、光照变化和单视角局限影响。少数研究在心血管患者中验证了其可靠性，且缺乏跨设备评估的公开数据集。为此，我们提出M3PD数据集，首个公开的双视角移动PPG数据集，包含60名参与者（其中47名为心血管患者）通过手机前后摄像头同步采集的面部与指尖视频。基于此双视角设置，我们进一步提出F3Mamba模型，采用Mamba架构融合面部与指尖视图的时序信息。该模型相比现有单视角基线，心率误差降低21.9%至30.2%，在复杂现实场景中表现更鲁棒。数据与代码：https://github.com/Health-HCI-Group/F3Mamba。
原文摘要 · Abstract (English)
Portable physiological monitoring is essential for early detection and management of cardiovascular disease, but current methods often require specialized equipment that limits accessibility or impose impractical postures that patients cannot maintain. Video-based photoplethysmography on smartphones offers a convenient noninvasive alternative, yet it still faces reliability challenges caused by motion artifacts, lighting variations, and single-view constraints. Few studies have demonstrated reliable application to cardiovascular patients, and no widely used open datasets exist for cross-device accuracy. To address these limitations, we introduce the M3PD dataset, the first publicly available dual-view mobile photoplethysmography dataset, comprising synchronized facial and fingertip videos captured simultaneously via front and rear smartphone cameras from 60 participants (including 47 cardiovascular patients). Building on this dual-view setting, we further propose F3Mamba, which fuses the facial and fingertip views through Mamba-based temporal modeling. The model reduces heart-rate error by 21.9 to 30.2 percent over existing single-view baselines while improving robustness in challenging real-world scenarios. Data and code: https://github.com/Health-HCI-Group/F3Mamba.
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