  Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive
applications in home surveillance, remote healthcare, road safety, and home
entertainment, among others. Most of the existing works are limited to the
activity classification of a single human subject at a given time. Conversely,
a more realistic scenario is to achieve simultaneous, multi-subject activity
classification. The first key challenge in that context is that the number of
classes grows exponentially with the number of subjects and activities.
Moreover, it is known that Wi-Fi sensing systems struggle to adapt to new
environments and subjects. To address both issues, we propose SiMWiSense, the
first framework for simultaneous multi-subject activity classification based on
Wi-Fi that generalizes to multiple environments and subjects. We address the
scalability issue by using the Channel State Information (CSI) computed from
the device positioned closest to the subject. We experimentally prove this
intuition by confirming that the best accuracy is experienced when the CSI
computed by the transceiver positioned closest to the subject is used for
classification. To address the generalization issue, we develop a brand-new
few-shot learning algorithm named Feature Reusable Embedding Learning (FREL).
Through an extensive data collection campaign in 3 different environments and 3
subjects performing 20 different activities simultaneously, we demonstrate that
SiMWiSense achieves classification accuracy of up to 97%, while FREL improves
the accuracy by 85% in comparison to a traditional Convolutional Neural Network
(CNN) and up to 20% when compared to the state-of-the-art few-shot embedding
learning (FSEL), by using only 15 seconds of additional data for each class.
For reproducibility purposes, we share our 1TB dataset and code repository.
