Abstract: Embodiments are disclosed for sleep staging using machine learning. In an embodiment, a method comprises: receiving, with at least one processor, sensor signals from a sensor, the sensor signals including at least motion signals and respiratory signals of a user; extracting, with the at least one processor, features from the sensor signals; predicting, with a machine learning classifier, that the user is asleep or awake based on the features; and computing, with the at least one processor, a sleep or wake metric based on whether the user is predicted to be asleep or awake.

Abstract: Systems and methods detect respiration of a subject, process respiration data, and based on the processed respiration data, perform extended monitoring. A monitoring system is configured with sensors that can be worn by the subject to provide the respiration data. The respiration data is processes to create a stability index. The stability index is used to, for example, determine sleep stages.