Abstract: In one embodiment, a method includes detecting, by a motion sensor of a mobile device worn by a user, multiple motion signals, each representing a motion of the user about one of a number of mobile-device axes defined by an orientation of the mobile device. The method further includes determining, for each of the multiple mobile-device axes, a ballistocardiogram (BCG) signal based on the motion signal corresponding to that mobile-device axis; selecting, based on a strength of the determined BCG signals, one or more particular mobile-device axes and corresponding motion signals for estimating a user's tidal volume; determining, based on the one or more selected motion signals, one or more breathing features; and estimating, by providing the one or more breathing features to a trained machine-learning model, the user's current tidal volume.

Abstract: In one embodiment, a method includes accessing, from a first sensor of a wearable device, motion data representing motion of a user and determining, from the motion data, an activity level of the user. The method includes selecting, based on the activity level, an activity-based technique for estimating the breathing rate of the user, which is used to determine a breathing rate of the user. The method further includes determining a quality associated with the breathing rate and comparing the determined quality with a threshold. If the determined quality is not less than the threshold, then the method includes using the determined breathing rate as a final breathing-rate determination for the user. If the determined quality is less than the threshold, then the method includes activating a second sensor of the wearable device and determining, based on data from the second sensor, a breathing rate for the user.