Towards a general intelligence and interface for wearable health data
Learning from a trillion minutes of sensor data
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.
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.
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.
