HBP-net for robust remote heart rate estimation using heartbeat probability
Xiaolang Ye, Caiying Zhou, Yuanwang Wei, Fried-Michael Dahlweid, Hong Sun, Chaochao Wang, Xianchao Zhang

TL;DR
This paper introduces HBP-Net, a new method for accurately estimating heart rate from facial videos, even during motion and lighting changes, by predicting heartbeat probability directly.
Contribution
The novel contribution is reframing heart rate estimation as a heartbeat probability detection problem, bypassing traditional signal reconstruction.
Findings
HBP-Net achieves competitive accuracy under static and motion conditions across multiple datasets.
The method demonstrates state-of-the-art performance on motion-intensive benchmark datasets.
An open-source code and unified evaluation framework are released for community use.
Abstract
Remote photoplethysmography (rPPG) enables contactless heart rate monitoring but remains vulnerable to motion and lighting changes. We address this by reframing heart rate estimation as a heartbeat detection problem, bypassing the need to reconstruct full blood volume pulse signals. Our approach, HBP-Net, predicts heartbeat probability directly from facial video using a spatiotemporal attention architecture, improving robustness while reducing computational complexity. Evaluated across multiple datasets—including a new motion-challenged benchmark—HBP-Net achieves competitive accuracy under static conditions and maintains performance as motion increases. This shift from signal reconstruction to probabilistic event detection offers a conceptually simpler and more resilient framework for rPPG. The method advances the feasibility of reliable, camera-based vital sign monitoring in real-world…
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Taxonomy
TopicsNon-Invasive Vital Sign Monitoring · Heart Rate Variability and Autonomic Control · ECG Monitoring and Analysis
