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

AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization