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.

翻译：近年来，基于Wi-Fi的手势识别引起了广泛关注。然而，现有研究主要集中在封闭集范式上，即所有测试手势均在训练时预定义。这在现实应用中带来了重大挑战，因为未知手势可能在测试时被误分类为已知类别。为解决这一问题，我们提出了WiOpen，一种鲁棒的基于Wi-Fi的开放集手势识别（OSGR）框架。实施OSGR需要应对由Wi-Fi感知中独特的不确定性所引发的挑战。这种由噪声和域造成的不确定性，导致收集的Wi-Fi感知数据呈现出广泛分散且不规则的数据分布。由此，产生了类别间的数据模糊性，以及难以定义合适决策边界以识别未知类别的挑战。为攻克这些挑战，WiOpen采用双重方法消除不确定性并定义精确的决策边界。首先，它在数据预处理阶段利用CSI比率处理噪声引起的不确定性。其次，它基于不确定性量化方法设计OSGR网络。在整个学习过程中，该网络有效缓解了源于域的不确定性。最终，网络利用样本邻居间的关系动态定义开放集决策边界，成功实现OSGR。在公开数据集上的全面实验证实了WiOpen的有效性。值得注意的是，与最先进方法相比，WiOpen在跨域任务中也展现出优越性。