Validation of non-contact sensor quantification of heart rate and respiratory rate dynamics using real-world pretraining and label-efficient fine-tuning on polysomnograms

Non-contact mechanical bed sensors can passively and longitudinally monitor the dynamics of cardiopulmonary physiology to detect changes from patient-specific baselines and facilitate care. This requires accurate longitudinal quantification of established metrics like respiratory rate (RR) and heart rate (HR) from the underlying raw waveforms, validated against ground truth labeled datasets like simultaneous polysomnography (PSG). Whereas head-to-head labeled datasets are scarce and costly to collect, unlabeled real-world datasets are often abundant. Here, we show that non-optimized heuristic algorithms can be used to soft-label large real-world data (>40M minutes across >50,000 nights) for