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

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; from a corpus of 2.75 million hours of nightly recordings from 145,857 individuals, we pretrain with participant-level contrastive learning. 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 two independent external clinical 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. A second pretraining stage, in which each minute predicts how the embeddings of neighbouring minutes differ from its own, further enriches the embeddings with sleep-stage, apnea, and heart-rate information while retaining age information. These results establish ambient, longitudinal mechanical biosignals as a viable modality for health foundation models.