SensorFM: Towards a general intelligence and interface for wearable health data

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SensorFM: Towards a general intelligence and interface for wearable health data

July 9, 2026<br>Xin Liu, Senior Research Scientist, and Daniel McDuff, Staff Research Scientist, Google Research

We present SensorFM, 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, SensorFM 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.

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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.<br>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.

Learning from a trillion minutes of sensor data<br>To build the pre-training corpus, we sampled 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. From each person we drew several weeks of data, yielding over two billion hours — more than a trillion minutes — of minute-resolution signals.<br>SensorFM ingests 34 one-minute aggregate features derived from five sensor modalities: photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), skin temperature, and altimetry. Together these capture heart rate and heart-rate variability, blood-oxygen saturation, sleep stages, motion and steps, skin conductance, and temperature over a full 24-hour window.<br>Rather than relying on labels, SensorFM learns through self-supervised reconstruction, building on the LSM-2 approach and its Adaptive and Inherited Masking (AIM) framework. This is a crucial design choice, because missing and fragmented data (e.g., stretches of time where data is not available) is the norm with wearable devices, caused by a variety of factors such as sensors’ power-cycle, devices coming off the wrist, power saving modes of operation, and sensors switching on and off. Conventional self-supervised methods assume complete, uninterrupted inputs and so are forced to either impute the gaps (which can introduce bias) or discard incomplete windows (which throws away valuable data). AIM takes neither path: it treats real-world missingness as a natural artifact and learns directly from incomplete recordings, combining the tokens inherited from genuine gaps with those artificially masked for the reconstruction objective and treating the two as equivalent. The result is a representation that is missingness-aware by construction. SensorFM does not just tolerate fragmented data, it uses it productively, as the generative results below show.

SensorFM is pre-trained on over a trillion minutes of multimodal sensor data via missing-aware masked reconstruction.

Scaling model and data together pays off<br>A central question for any foundation model is whether scale translates into capability. We ran a systematic set of scaling experiments, spanning four orders of magnitude in both pre-training data volume (from roughly 2 million to 2 billion sensor-hours) and model size (from 100K to 100M parameters).<br>The result is a clean, encouraging signal: pre-training loss...