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: In an example method, a computing device obtains sensor data generated by one or more accelerometers and one or more gyroscopes over a time period, including an acceleration signal indicative of an acceleration measured by the one or more accelerometers over a time period, and an orientation signal indicative of an orientation measured by the one or more gyroscopes over the time period. The one or more accelerometers and the one or more gyroscopes are physically coupled to a user walking along a surface. The computing device identifies one or more portions of the sensor data based on one or more criteria, and determines characteristics regarding a gait of the user based on the one or more portions of the sensor data, including a walking speed of the user and an asymmetry of the gait of the user.

Abstract: One or more electronic device may use motion and/or activity sensors to estimate a user's 6 minute walking distance. In particular, because users typically walk at less than their maximum output and in imperfect conditions, control circuitry within the device(s) may rely on walks of shorter distances to estimate the 6 minute walking distance. For example, the control circuitry may gather activity information for the user, such as heart rate, calories burned, and step count, and analyze a distance component and a speed component for periods in which the user has walked. Individual 6 minute walk distance estimates may be generated based on each of the activity information, distance component, and speed component. The distance and speed estimates may be corrected for walking behaviors that deviate from an ideal testing environment, and may then be fused with the activity estimate to generate a final 6 minute walk distance estimate.

Abstract: In some implementations, a method includes: while presenting a 3D environment, obtaining user profile and head pose information for a user; determining locations for visual cues within the 3D environment for a first portion of a guided stretching session based on the user profile and head pose information; presenting the visual cues at the determined locations within the 3D environment and a directional indicator; and in response to detecting a change to the head pose information: updating a location for the directional indicator based on the change to the head pose information; and in accordance with a determination that the change to the head pose information satisfies a criterion associated with a first visual cue among the visual cues, providing at least one of audio, haptic, or visual feedback indicating that the first visual cue has been completed for the first portion of the guided stretching session.

Abstract: In an example method, a computing device obtains sensor data generated by one or more accelerometers and one or more gyroscopes over a time period, including an acceleration signal indicative of an acceleration measured by the one or more accelerometers over a time period, and an orientation signal indicative of an orientation measured by the one or more gyroscopes over the time period. The one or more accelerometers and the one or more gyroscopes are physically coupled to a user walking along a surface. The computing device identifies one or more portions of the sensor data based on one or more criteria, and determines characteristics regarding a gait of the user based on the one or more portions of the sensor data, including a walking speed of the user and an asymmetry of the gait of the user.