Pretraining on massive unlabeled wearable data creates reusable health representations that work across diverse prediction tasks with little labeled data—similar to how large language models work, but for physiological signals.
Researchers built a foundation model trained on over one trillion minutes of unlabeled wearable sensor data from five million people to predict health outcomes. The model learns general patterns from this massive dataset, then adapts to specific health tasks (like predicting heart disease or sleep quality) with minimal labeled examples.