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

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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. 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

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…

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