BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing
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Computer Science > Machine Learning
Title: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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET) |
| Cite as: | arXiv:2606.07692 [cs.LG] |
| (or arXiv:2606.07692v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07692
arXiv-issued DOI via DataCite (pending registration)
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