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📄 Abstract
Abstract: 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.
Key Contributions
Introduces the M3PD dataset, the first publicly available dual-view mobile PPG dataset captured using smartphone front and rear cameras from both healthy individuals and cardiovascular patients. Proposes F3Mamba, a novel approach leveraging this dual-view setting for improved physiological monitoring.
Business Value
Enables the development of more accessible and convenient tools for continuous physiological monitoring, particularly for cardiovascular health, potentially leading to earlier disease detection and better patient management.