MuSense: Multiperson Continuous Activity Sensing Using Commodity Wi-Fi

Wi-Fi-based continuous activity sensing is of great importance to personal healthcare, security monitoring, and healthy lifestyle assessment. However, it remains challenging to understand multiperson continuous activities as the Wi-Fi signals reflected by each person are mixed up in the Wi-Fi channel state information (CSI). To this end, we present MuSense, the first Wi-Fi-based system that enables multiperson continuous activity segmentation and recognition using commodity devices. In MuSense, we design a Wi-Fi network interface card (NIC) combination and calibration (NICCC) algorithm to construct a high-resolution receiving array and calibrate the CSI measurement noises of this array. On this basis, we propose a multiperson reflection signal separation (MPRSS) algorithm to completely and practically separate each person’s Wi-Fi reflection signals, obtaining the amplitude attenuation and phase shift corresponding to each person’s activities on the subcarrier dimension. Finally, we design an unsupervised adversarial continuous activity sensing network (UACAS-Net), with a two-stage adversarial training method to achieve generalized multiperson continuous activity sensing. Through two-stage adversarial training, UACAS-Net can capture distinguishing features of continuous activities from separated reflection signal streams in the source and target domains while minimizing the feature domain discrepancies to segment and recognize each person’s continuous activities in different domains. Intensive experiments are conducted under three different scenarios and the results demonstrate the effectiveness and practicality of MuSense for multiperson continuous activity sensing.