Abstract: Systems and methods for person identification using wireless signals is disclosed. Channel state information is collected from wireless signals received by a wireless receiver. The channel state information (CSI) is pre-processed to determine a gait and biometric features including body shape and height associated with a particular person based on selected CSI segments, which are annotated with a class indicative of the gait. The particular person is identified, using a machine learning model, based on the gait and the other biometric features, wherein the machine learning model is trained using classifier training and training data including the gait and information from the CSI segments, from which one or more parameters is determined. After training, the person can be identified based on packets from received wireless signals. The method further includes controlling a machine, in response to detecting the presence of the person, based on established usage patterns.

Abstract: A method for determining a status of a person using wireless signals includes annotating selected channel state information segments with a class indicative of a status of the person based on a gait and at least some of a plurality of biometric features of the person. The method also includes identifying the status of the person based on the gait and the at least some of the plurality of biometric features of the person, determining one or more settings of a machine corresponding to the status of the person, and controlling, by a computer system and using the one or more settings, operation of the machine in response to detecting the status of the person.

Abstract: A method for identifying an event in a room using wireless signals includes pre-processing channel state information to determine one or more aspects a person in a room based on selected channel state information segments, and identifying, using a machine learning model, an event in the room based on the one or more aspects of the person in the room. The method also includes determining, using the machine learning model and based on the event in the room, one or more settings of a machine, and controlling, using the one or more settings, operation of the machine in response to identifying the event in the room.