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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint evaluates whether unsupervised signal-processing pseudo-labels can replace contact sensor labels for training deep learning models to estimate heart rate from video. Results are mixed: pseudo-labeling performs better when video–signal synchronization is poor, but supervised contact sensor training performs better in cross-dataset settings with good synchronization; label quality and outlier handling significantly affect performance.
Systematic methodological evaluation; uncontrolled comparison study. Datasets with video and ground truth heart rate signals; synchronization quality varied between datasets. No participant eligibility criteria or setting described.. Intervention: Training deep learning rPPG models using pseudo-labels extracted via unsupervised signal-processing methods. Compared with: Training deep learning rPPG models using contact sensor (supervised) labels.
For datasets with imperfect synchronization, pseudo-label approach outperforms supervised training on contact sensors For datasets with good synchronization, within-dataset evaluation shows no significant difference between training methods Cross-dataset evaluation favors supervised training with good synchronization
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If validated in peer review, this work could reduce the burden of collecting precisely synchronized video and contact sensor data for training contactless heart rate estimation systems. However, the mixed results and lack of clinical validation limit immediate applicability to clinical decision-making.
This is an unreviewed methodological study investigating pseudo-labeling as an alternative to contact sensors for training deep learning models in remote heart rate estimation; it reports mixed results without a clear clinical validation or direct patient outcome.
As stated by the source record.
If validated in peer review, this work could reduce the burden of collecting precisely synchronized video and contact sensor data for training contactless heart rate estimation systems. However, the mixed results and lack of clinical validation limit immediate applicability to clinical decision-making.
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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.
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