BFA-Sense: Learning Beamforming Feedback Angles for Wi-Fi Sensing

In this paper, we propose BFA-Sense, a completely novel approach to implement standard-compliant Wi-Fi sensing applications. Wi-Fi sensing enables game-changing applications in remote healthcare, home entertainment, and home surveillance, among others. However, existing work leverages the manual extraction of the uncompressed channel state information (CSI) from Wi-Fi chips, which is not supported by the 802.11 standard-compliant devices and hence requires the use of specialized equipment. On the contrary, BFA-Sense leverages the compressed beamforming feedback angles (BFAs) transmitted during the standard-compliant sounding procedure to characterize the propagation environment. Conversely from the uncompressed CSI, BFAs (i) can be recorded without any firmware modification, and (ii) allows a single monitor device to simultaneously capture the channels between the access point and all the stations, thus providing much better sensitivity. We evaluate BFA-Sense through an extensive data collection campaign with three subjects performing twenty different activities in three different environments. We assess the cross-domain adaptability of BFA-Sense through embedding learning for tackling unseen environments with a few samples from the new environment. The results show that the proposed BFAs-based approach achieves about 11% more accuracy when compared to CSI-based prior work.