Estimates suggest that billions of wearable devices are now in use, precisely tracking heart rate, movement, skin temperature, blood-oxygen levels, and sleep across days, weeks, and months. This continuous, longitudinal stream of physiology and behavior provides one of the most promising raw materials for preventive, personalized health. Yet turning those low-level signals into meaningful insights remains hard. First, baseline physiology, lifestyle, and health vary enormously from person to person, so a pattern that signals risk in one individual may not in another. Second, the labels needed to train models — confirmed diagnoses, lab results, validated questionnaires — are expensive, slow to collect, and essentially impossible to gather retrospectively. As a result, most wearable health models have been built one outcome at a time, with bespoke, supervised pipelines that target a narrow endpoint and struggle to generalize across the full breadth of human health.
In “Towards a General Intelligence and Interface for Wearable Health Data”, we take a different approach. We introduce SensorFM, a Large Sensor Foundation Model that learns directly from unlabeled wearable data at population scale. Pre-trained on over one trillion minutes of multimodal sensor signals drawn from five million consented participants, SensorFM learns a single, reusable representation of sensed human physiology — one that transfers across cardiovascular, metabolic, sleep, and mental health, as well as lifestyle and demographic factors. To our knowledge, this is the largest and most diverse wearable dataset used to train a model to date.
