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

TL;DR

AirFi is a novel WiFi gesture recognition system that uses domain generalization to accurately recognize gestures in unseen environments without needing new environment data, outperforming existing methods.

Contribution

AirFi introduces a domain generalization framework for WiFi-based gesture recognition that does not require environment-specific data collection, enhancing robustness and practicality.

Findings

Outperforms state-of-the-art methods in unseen environments

Remains robust without collecting new environment data

Can be improved with few-shot learning techniques

Abstract

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.