SOURCE-LINKED INTELLIGENCE
Beyond Contact Sensors: Deep learning with Pseudo-Labeling for remote Photoplethysmography
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-09T11:00:12.000Z
First collected: 2026-09-20T19:32:24.350Z. This is not the publication date.