BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing
This work establishes ambient mechanical biosignals as a viable modality for health foundation models, enabling passive, longitudinal health monitoring without user effort.
BCG-FM is the first foundation model for ambient ballistocardiography (BCG) signals, pretrained on 2.75 million hours of nightly recordings from 145,985 individuals. It achieves 3.26-year MAE on biological-age estimation and outperforms a fully supervised baseline with only 500 labeled participants.
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.