Google's SensorFM Beats 34 of 35 Health Tasks Trained on a Trillion Minutes

Most health AI models are built one condition at a time: a separate model for sleep, another for heart risk, another for depression. Each needs its own labeled dataset, its own architecture, and its own training run. SensorFM, a new foundation model from Google Research, bets that a single, massive pre-training run on raw wearable data can replace all of that -- and the results are hard to argue with.

SensorFM is a foundation model for wearable health pre-trained on more than one trillion minutes of sensor data from five million people. By co-scaling model size and data, it learns a general-purpose representation of human physiology that transfers to 35 health prediction tasks, supports label-efficient adaptation and data infilling, and can serve as a grounding tool for a Personal Health Agent. To Google's knowledge, this is the largest and most diverse wearable dataset used to train a model to date.

The problem with wearable health AI today

While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, converting low-level sensor data into representations capable of characterizing higher-level health states is difficult due to high phenotypic diversity and variation in individual baseline health, physiology, and lifestyle factors. Moreover, collecting wearable data paired with health outcome annotations is laborious and expensive, and retrospective annotation remains practically unfeasible, contributing to a scarcity of data with high-quality labels.

This is the core bottleneck. You can collect billions of hours of heart rate and step data passively, but the moment you need a label -- a confirmed depression diagnosis, a blood glucose reading, a validated sleep study -- you need clinicians, IRB approvals, and years of prospective data collection. SensorFM sidesteps this entirely by learning from the raw signals alone.

A trillion minutes of unlabeled physiology

The pre-training corpus was built from de-identified data from five million people who had consented to the use of their data for health and wellness research, captured between September 2024 and September 2025. The dataset spans more than 100 countries, all 50 U.S. states, and over 20 Fitbit and Pixel Watch device models.

SensorFM ingests 34 one-minute aggregate features derived from five sensor types:

• PPG (photoplethysmography) -- heart rate, heart rate variability, blood-oxygen saturation
• Accelerometry -- motion, steps, sleep stages
• Electrodermal activity (EDA) -- skin conductance, a proxy for stress arousal