  Recent years have witnessed a growing interest in Wi-Fi-based gesture
recognition. However, existing works have predominantly focused on closed-set
paradigms, where all testing gestures are predefined during training. This
poses a significant challenge in real-world applications, as unseen gestures
might be misclassified as known classes during testing. To address this issue,
we propose WiOpen, a robust Wi-Fi-based Open-Set Gesture Recognition (OSGR)
framework. Implementing OSGR requires addressing challenges caused by the
unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and
domains, leads to widely scattered and irregular data distributions in
collected Wi-Fi sensing data. Consequently, data ambiguity between classes and
challenges in defining appropriate decision boundaries to identify unknowns
arise. To tackle these challenges, WiOpen adopts a two-fold approach to
eliminate uncertainty and define precise decision boundaries. Initially, it
addresses uncertainty induced by noise during data preprocessing by utilizing
the CSI ratio. Next, it designs the OSGR network based on an uncertainty
quantification method. Throughout the learning process, this network
effectively mitigates uncertainty stemming from domains. Ultimately, the
network leverages relationships among samples' neighbors to dynamically define
open-set decision boundaries, successfully realizing OSGR. Comprehensive
experiments on publicly accessible datasets confirm WiOpen's effectiveness.
Notably, WiOpen also demonstrates superiority in cross-domain tasks when
compared to state-of-the-art approaches.
