# HBP-net for robust remote heart rate estimation using heartbeat probability

## 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.

## Key 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 settings such as telehealth, fitness tracking, and continuous patient assessment.

•Introduces a probabilistic approach for robust remote heart rate estimation•Achieves reliable performance under real-world motion and lighting changes•Demonstrates state-of-the-art performance on motion-intensive benchmark datasets•Releases open-source code and a unified evaluation framework for the community

Introduces a probabilistic approach for robust remote heart rate estimation

Achieves reliable performance under real-world motion and lighting changes

Demonstrates state-of-the-art performance on motion-intensive benchmark datasets

Releases open-source code and a unified evaluation framework for the community

Health sciences