M ³ PD Dataset: Enabling Dual-view Photoplethysmography on Smartphones in Lab and Clinical Settings

Portable physiological monitoring is essential for early detection and management of cardiovascular disease, but current methods often require specialized equipment that limits accessibility or imposes impractical postures that patients cannot maintain. Video-based photoplethysmography on smartphones offers a convenient non-invasive alternative, yet it still faces reliability challenges caused by motion artifacts, lighting variations, and single-view constraints. Few studies have demonstrated that this technology can be reliably applied to physiological monitoring of cardiovascular patients, and no widely used open datasets exist for researchers to examine its cross-device accuracy. To address these limitations, we introduce the M 3 PD 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 the F 3 Mamba, which fuses the facial and fingertip views through Mamba-based temporal modeling. The model reduces heart-rate error by 21.9-30.2% over existing single-view baselines while showing enhanced robustness across challenging real-world scenarios, and further achieves a 4.4-16.4% MAE reduction over the best standard dual-view fusion baselines. Data and code are released at https://github.com/Health-HCI-Group/F3Mamba.