CogAdapt adapts clinical ECG foundation models to 3-lead wearable signals for cognitive load assessment via a LeadBridge adapter and ProFine progressive fine-tuning, outperforming scratch-trained models with macro-F1 of 0.626 and 0.768 on public datasets under leave-one-subject-out validation.
Simper: Simple self-supervised learning of periodic tar- gets.arXiv preprint arXiv:2210.03115
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FCUS-rPPG is a fast-converging unsupervised rPPG framework using a spectrally shared backbone and multi-level gradient optimization to reach SOTA cross-dataset performance in one epoch.
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CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation
CogAdapt adapts clinical ECG foundation models to 3-lead wearable signals for cognitive load assessment via a LeadBridge adapter and ProFine progressive fine-tuning, outperforming scratch-trained models with macro-F1 of 0.626 and 0.768 on public datasets under leave-one-subject-out validation.
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FCUS-rPPG: A Fast-Converging Unsupervised Framework for Remote Photoplethysmography via Gradient Oscillation Suppression
FCUS-rPPG is a fast-converging unsupervised rPPG framework using a spectrally shared backbone and multi-level gradient optimization to reach SOTA cross-dataset performance in one epoch.