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

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AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization
Title:
AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization
Journal Title:
IEEE Transactions on Mobile Computing
Publication Date:
20 December 2022
Citation:
Wang, D., Yang, J., Cui, W., Xie, L., & Sun, S. (2022). AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization. IEEE Transactions on Mobile Computing, 1–12. https://doi.org/10.1109/tmc.2022.3230665
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.
License type:
Publisher Copyright
Funding Info:
This research of the first author is supported by Agency for Science, Technology and Research (Singapore) under AGS scholarship.
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1558-0660
2161-9875
1536-1233
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