Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
This work addresses the need for better health prediction models in wearable technology by focusing on behavioral data, offering incremental improvements through tailored foundation model design.
The paper tackled the problem of improving health predictions from wearable devices by developing foundation models specifically for behavioral data, which are often more informative than raw sensor data, and achieved strong performance across 57 health-related tasks, including sleep prediction, using over 2.5B hours of data from 162K individuals.
Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been primarily applied to low-level sensor data, despite behavioral data often being more informative due to their alignment with physiologically relevant timescales and quantities. We develop foundation models of such behavioral signals using over 2.5B hours of wearable data from 162K individuals, systematically optimizing architectures and tokenization strategies for this unique dataset. Evaluated on 57 health-related tasks, our model shows strong performance across diverse real-world applications including individual-level classification and time-varying health state prediction. The model excels in behavior-driven tasks like sleep prediction, and improves further when combined with representations of raw sensor data. These results underscore the importance of tailoring foundation model design to wearables and demonstrate the potential to enable new health applications.