TS2TC combines cross-temporal fusion generative anchor pretraining with dual-process transfer to achieve 2.49% lower RMSE than prior methods on PPG parameter estimation using only 10% labeled data.
IEEE Transac- tions on Biomedical Engineering 60, 1946–1953
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A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography
TS2TC combines cross-temporal fusion generative anchor pretraining with dual-process transfer to achieve 2.49% lower RMSE than prior methods on PPG parameter estimation using only 10% labeled data.