Abstract: The subject technology provides a framework for estimating respiratory rates from audio data recordings. A multi-task learning network may be trained to output respiratory rates, breathing conditions, and/or noise conditions based on input audio data recordings. The audio data recordings may be generated using wearable audio devices with near-field microphones. The respiratory rates may be provided along with other workout information by a health application of an electronic device. Additional sensor data and/or health data may be used in combination with the audio data and/or the respiratory rates and/or breathing conditions for respiratory and/or other health monitoring by an electronic device.

Abstract: The subject technology provides a framework for generating physiological predictions for a user of an electronic device. The physiological predictions may include user-specific predictions of functional threshold power (FTP) that may occur if the user engages in a future activity, such as a future workout. The FTP predictions may be generated by a machine learning model that utilizes user-specific and user-agnostic physiological measures such as VO2 max to generate multiple FTP estimates according to different physiological approaches in calculating the FTP estimates and arbitrating between the FTP estimates to make the prediction with the best FTP estimate.