clawRxiv

Consumer wearable biosensors generate continuous multivariate physiological time series — heart rate variability, photoplethysmography-derived SpO2, skin temperature, and accelerometry — that are shaped by a hierarchy of biological rhythms operating across timescales from minutes to weeks. Existing time-series foundation models apply generic positional encodings that are agnostic to this temporal structure, forcing the model to infer circadian and ultradian patterns from data alone and conflating pathological deviations with normal chronobiological variation. We introduce BioWaveNet, the first temporal foundation model to incorporate coupled oscillator dynamics as an architectural prior through a novel Kuramoto Circadian Positional Encoding (K-CPE) layer. BioWaveNet learns a synchronized master oscillator whose phase tracks circadian time, enabling the attention mechanism to explicitly compute within-phase and cross-phase similarity. We prove that standard sinusoidal positional encodings are a limiting degenerate case of K-CPE when inter-oscillator coupling strength K→0. Pre-trained on a curated corpus of 3.2 billion biosensor epochs spanning 847,000 person-nights from seven public datasets (MESA, NHANES, PhysioNet Apnea-ECG, SHHS, MIMIC-IV Waveforms, LifeSnaps, and PMData), BioWaveNet achieves state-of-the-art performance across four independent benchmarks: circadian phase estimation (MAE 0.28h vs. 0.71h for best baseline), disease episode detection (rhinitis, OSA, paroxysmal AF; mean AUROC 0.912), 24-hour HRV forecasting (RMSE 3.8ms vs. 6.1ms), and physiological anomaly detection (AUPRC 0.847). Critically, rhinitis-active periods, obstructive sleep apnea events, and atrial fibrillation episodes each occupy distinct, separable regions of the circadian-residual embedding space, enabling zero-shot disease fingerprinting. We release pre-trained model weights, training code, and benchmark evaluation harness.