Beyond Contact Sensors: Deep learning with Pseudo-Labeling for remote Photoplethysmography

2026-09-09

AI Summary

研究通过使用无监督信号处理方法生成的伪标签来替代接触式传感器标签，以减少深度学习rPPG方法对标注数据集的依赖，同时保持性能。

Abstract

Heart rate is a critical biomarker of health, and remote photoplethysmography (rPPG) enables its contactless estimation from video data for telemedicine applications. Recent advancements in deep learning based rPPG methods achieve state-of-the-art results, outperforming classical signal-processing methods in complex scenarios. However, deep learning methods depend on datasets with precise synchronization between videos and ground truth signals collected via contact sensors, whereas signal-processing-based methods do not. To address this dependence on labeled datasets, which are labor-intensive to collect, we investigate under which circumstances pseudo-labels extracted using unsupervised signal-processing methods can replace contact sensors labels for training deep learning methods. Our systematic evaluations found that for datasets with imperfect synchronization, the pseudo-label approach outperforms supervised training on contact sensors. For datasets with good synchronization, results are mixed: within-dataset evaluation shows no significant difference between training methods, while cross-dataset evaluation favors supervised training. However, removing a single outlier participant significantly improves the pseudo-label approach's cross-dataset performance, highlighting the importance of label quality. These results demonstrate that signal-processing methods can generate valid training signals for deep learning models, reducing dependency on labor-intensive dataset collection while maintaining competitive performance.

Problem

• remote photoplethysmography
• deep learning
• pseudo-labeling
• synchronization
• contact sensors

Innovation

• Pseudo-Labeling
• Remote Photoplethysmography
• Deep Learning
• Unsupervised Signal-Processing