Wearable devices generate enormous amounts of multimodal sensor data (heart rate, motion, skin conductance, temperature, etc.), but most AI models for health are narrow, task-specific, and require large amounts of labeled data. This limits scalability and generalization in real-world settings where data is often fragmented or incomplete.

Google researchers introduce SensorFM, a large sensor foundation model pre-trained self-supervised on over one trillion minutes of wearable data from more than five million consented participants across diverse devices and geographies. It learns a general-purpose representation of human physiology using a missingness-aware framework (AIM) that treats real-world data gaps as meaningful signals rather than noise.

The idea that healthcare simply needs its own UPI moment is seductive but fundamentally flawed. It trivializes one of the most complex information ecosystems ever built.

UPI succeeded because payments rest on a simple universal primitive: identity, authorization, amount, and settlement. A rupee is a rupee. The semantics are finite, deterministic, and often reversible.