  WiFi-based smart human sensing technology enabled by Channel State
Information (CSI) has received great attention in recent years. However,
CSI-based sensing systems suffer from performance degradation when deployed in
different environments. Existing works solve this problem by domain adaptation
using massive unlabeled high-quality data from the new environment, which is
usually unavailable in practice. In this paper, we propose a novel augmented
environment-invariant robust WiFi gesture recognition system named AirFi that
deals with the issue of environment dependency from a new perspective. The
AirFi is a novel domain generalization framework that learns the critical part
of CSI regardless of different environments and generalizes the model to unseen
scenarios, which does not require collecting any data for adaptation to the new
environment. AirFi extracts the common features from several training
environment settings and minimizes the distribution differences among them. The
feature is further augmented to be more robust to environments. Moreover, the
system can be further improved by few-shot learning techniques. Compared to
state-of-the-art methods, AirFi is able to work in different environment
settings without acquiring any CSI data from the new environment. The
experimental results demonstrate that our system remains robust in the new
environment and outperforms the compared systems.
