The adaptive physiology-informed correction algorithm increased the proportion of accurate remote heart rate measurements (MAE ≤ 10 BPM) from 46.26% to 84.14% on the LGI-PPGI dataset.
Does an adaptive physiology-informed correction algorithm improve the accuracy of remote photoplethysmography heart-rate monitoring compared to existing methods?
A novel physiology-informed adaptive correction algorithm significantly improves the accuracy and reliability of contactless heart rate monitoring from facial videos, even under challenging motion and low-light conditions.
Absolute Event Rate: 84.14% vs 46.26%
Contactless heart rate (HR) monitoring demonstrates significant potential for mobile health and telemedicine, but current remote photoplethysmography (rPPG) approaches remain vulnerable to various noise sources. While existing research has emphasized signal-level enhancement, correcting erroneous HR estimates remains underexplored. We present a plug-and-play adaptive correction algorithm that leverages cardiac dynamics constraints, adjusting HR estimates based on physiological priors of HR elevation and recovery. By mapping HR frequencies to indices and applying adaptive corrections, our method significantly reduces measurement errors with minimal computational load, even under challenging conditions. Across three public datasets, the algorithm increased the proportion of accurate measurements (mean absolute error ≤ 10 beats per minute) from 46.26% to 84.14% (LGI-PPGI), 48.03% to 69.21% (BUAA-MIHR), and 92.22% to 96.67% (UBFC-rPPG), outperforming existing correction techniques. The lightweight design facilitates seamless edge-side integration, providing a scalable solution for enhancing the reliability of contactless HR monitoring in mobile and remote healthcare settings.
Tian et al. (Tue,) conducted a other in Heart rate monitoring. Adaptive physiology-informed correction algorithm vs. Uncorrected rPPG and existing correction methods was evaluated on Proportion of accurate measurements (MAE ≤ 10 BPM) on LGI-PPGI dataset. The adaptive physiology-informed correction algorithm increased the proportion of accurate remote heart rate measurements (MAE ≤ 10 BPM) from 46.26% to 84.14% on the LGI-PPGI dataset.