Abstract: Detecting an adverse health event and responding to an emergency event can include updating a current context profile of a user based on signals generated by one or more sensors of a device, the signals generated in response to one or more physically measurable phenomena associated with the user. A context-aware baseline profile having a same context as the current context profile can be selected, the context-aware baseline profile selected from a plurality of context-aware baseline profiles having different contexts. Features of the current context profile can be compared with corresponding features of the context-aware baseline profile of the user having the same context as the current context profile. A remedial action can be initiated in response to recognizing, based on the comparing, an occurrence of a possible adverse health event affecting the user.

Abstract: Detecting and identifying a predetermined health event can include detecting a potential occurrence of the predetermined health event for a user by processing in real-time motion signals corresponding to motion of the user. A likelihood that the potential occurrence is an actual occurrence of the predetermined health event can be determined based on template matching of the motion signals. In response to determining that the likelihood exceeds a predetermined threshold, audio signals coinciding in time with the motion of the user can be processed using one or more layers of a multilayered audio event classifier.

Abstract: Passively monitoring a user's breathing with a device can include identifying breathing modes of the user's breathing and responsive to detecting a trigger mode based on the identifying, generating an instruction adapted to the trigger mode. The instruction can be conveyed to the user via the device. The monitoring can include determining phases of the user's breathing with the device. Determining the phases can include receiving acoustic signals generated by an acoustic sensor in response to a user's breathing and generating acoustic data comprising features extracted from the acoustic signals. Phases of the user's breathing can be determined by classifying the acoustic data using a machine learning model trained based on signal processing of motion signals generated by a motion sensor in response to human breathing motions. Though trained using signal processing of motion signals, the machine learning model is trained to classify acoustic data.