Though a majority of past work has considered modeling low-level sensor data (or simple features thereof), higher-level behavioral information from wearables such as physical activity, cardiovascular fitness, and mobility metrics, are the natural data type to help solve these detection tasks. Unlike raw sensors, these higher-level behavioral metrics are calculated using carefully validated algorithms derived from the raw sensors. These metrics are intentionally chosen by experts to align with physiologically relevant quantities and health states. Importantly, these data are sensitive to an individual’s behaviors, rather than being driven purely by physiology. These characteristics make behavioral data particularly promising for such health detection tasks. For example, mobility metrics that characterize walking gait and overall activity levels may be important behavioral factors to help detect a changing health state such as pregnancy.”, Apple researchers developed an AI model trained on behavioral data collected from wearables, and it performed surprisingly well., A new Apple-backed AI model trained on Apple Watch behavioral data can now predict a wide range of health conditions more accurately than traditional.