  Having been studied for more than a decade, Wi-Fi human sensing still faces a
major challenge in the presence of multiple persons, simply because the limited
bandwidth of Wi-Fi fails to provide a sufficient range resolution to physically
separate multiple subjects. Existing solutions mostly avoid this challenge by
switching to radars with GHz bandwidth, at the cost of cumbersome deployments.
Therefore, could Wi-Fi human sensing handle multiple subjects remains an open
question. This paper presents MUSE-Fi, the first Wi-Fi multi-person sensing
system with physical separability. The principle behind MUSE-Fi is that, given
a Wi-Fi device (e.g., smartphone) very close to a subject, the near-field
channel variation caused by the subject significantly overwhelms variations
caused by other distant subjects. Consequently, focusing on the channel state
information (CSI) carried by the traffic in and out of this device naturally
allows for physically separating multiple subjects. Based on this principle, we
propose three sensing strategies for MUSE-Fi: i) uplink CSI, ii) downlink CSI,
and iii) downlink beamforming feedback, where we specifically tackle signal
recovery from sparse (per-user) traffic under realistic multi-user
communication scenarios. Our extensive evaluations clearly demonstrate that
MUSE-Fi is able to successfully handle multi-person sensing with respect to
three typical applications: respiration monitoring, gesture detection, and
activity recognition.
