A new intervention-based SSL paradigm for rPPG uses video editing and falsifiability checks to learn the true physiological signal instead of dominant artifacts.
arXiv preprint arXiv:1905.02419 , year =
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LQ-rPPG introduces label quantization and coarse-to-fine hierarchical supervision to mitigate noise in contact PPG labels for improved remote photoplethysmography estimation.
DRP-Net extracts phase-shifted rPPG signals from facial and acral regions for heart rate; BBP-Net uses their temporal and phase features plus a bounded sigmoid to estimate SBP/DBP, reporting MAEs of 1.78 BPM / 10.19 mmHg / 7.09 mmHg on MMSE-HR.
citing papers explorer
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Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography
A new intervention-based SSL paradigm for rPPG uses video editing and falsifiability checks to learn the true physiological signal instead of dominant artifacts.
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LQ-rPPG: A Label-Quantized Coarse-to-Fine Learning Framework for Remote Physiological Measurement
LQ-rPPG introduces label quantization and coarse-to-fine hierarchical supervision to mitigate noise in contact PPG labels for improved remote photoplethysmography estimation.
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Phase-shifted remote photoplethysmography for estimating heart rate and blood pressure from facial video
DRP-Net extracts phase-shifted rPPG signals from facial and acral regions for heart rate; BBP-Net uses their temporal and phase features plus a bounded sigmoid to estimate SBP/DBP, reporting MAEs of 1.78 BPM / 10.19 mmHg / 7.09 mmHg on MMSE-HR.