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Google Research unveils SensorFM: trained on a trillion minutes of wearable data, wins 33 of 35 health tasks

Pretrained on 5 million people, 2 billion hours of wearable data — a frozen encoder with a single linear head beats supervised baselines on 34 of 35 health tasks.
⚑ This is Google Research's own official blog post introducing its own model, SensorFM. The data scale, task win rates, and blind-test conclusions below all come from Google's official account and its paper, and have not yet been independently reproduced by a third party.
1Background

Wearables have piled up a decade of data, most of it never really put to use

Over the past decade, watches and wristbands have logged massive amounts of heart rate, blood oxygen, sleep, and activity data. Yet most of this data just sits dormant on the device, or gets used for one narrow purpose: counting today's steps, checking how well you slept. Getting it to actually predict health risk runs into three walls with the traditional approach.

Primary source and reference