Wireless based occupancy detection and activity monitoring

Abstract

Examples for wireless based occupancy detection are described. In one example, a described method comprises: transmitting, by each of a plurality of transmitters, a respective wireless signal through a wireless channel of a venue; receiving, by a receiver, the respective wireless signal, the received wireless signal different from the transmitted wireless signal due to the wireless channel and a motion of a user when the user is present in the venue; obtaining a plurality of time series of channel information (TSCI) of the wireless channel; computing a plurality of autocorrelation function (ACF) segments based on the plurality of TSCI; generating a plurality of feature maps using a first deep learning model that is shared by the plurality of ACF segments, wherein each ACF segment is input into the first deep learning model individually to generate a respective feature map; inputting the plurality of feature maps together into a second deep learning model to generate an aggregate representation that is independent of a quantity and locations of the transmitters; computing a probability of user presence based on the aggregate representation; and detecting a presence of the user in the venue based on a threshold and the probability of user presence.