Autosen: improving automatic wifi human sensing through cross-modal
  autoencoder
Qian Gao Yanling Hao Yuanwei Liu
WiFi human sensing is highly regarded for its low-cost and privacy advantages
in recognizing human activities. However, its effectiveness is largely confined
to controlled, single-user, line-of-sight settings, limited by data collection
complexities and the scarcity of labeled datasets. Traditional cross-modal
methods, aimed at mitigating these limitations by enabling self-supervised
learning without labeled data, struggle to extract meaningful features from
amplitude-phase combinations. In response, we introduce AutoSen, an innovative
automatic WiFi sensing solution that departs from conventional approaches.
AutoSen establishes a direct link between amplitude and phase through automated
cross-modal autoencoder learning. This autoencoder efficiently extracts
valuable features from unlabeled CSI data, encompassing amplitude and phase
information while eliminating their respective unique noises. These features
are then leveraged for specific tasks using few-shot learning techniques.
AutoSen's performance is rigorously evaluated on a publicly accessible
benchmark dataset, demonstrating its exceptional capabilities in automatic WiFi
sensing through the extraction of comprehensive cross-modal features.