BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing

Abstract:Foundation models for wearable biosignals have matched or exceeded supervised specialists across a range of clinical tasks, yet all rely on modalities that require deliberate user action--wearing a device or visiting a sleep lab. We introduce BCG-FM, the first foundation model for ambient mechanical biosignals. A piezoelectric sensor embedded in the bed surface records ballistocardiography (BCG) each night without user effort; we pretrain BCG-FM with participant-level contrastive learning and using a total of 2.75 million hours of nightly recordings from 145,985 individuals, the largest raw-waveform biosignal pretraining corpus to date. Frozen BCG-FM embeddings achieve 3.26-year MAE on biological-age estimation (the lowest reported for any ambient, contactless modality) and yield clinically relevant discrimination across 15 self-reported health conditions and three independent external cohorts. Pretrained representations from only 500 labeled participants outperform a fully supervised baseline trained on 3,372, and representation quality scales log-linearly with contrastive batch size. These results establish ambient, longitudinal mechanical biosignals as a viable modality for health foundation models.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)

arXiv:2606.07692 [cs.LG]

(or arXiv:2606.07692v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.07692 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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