In this paper, we propose GesFi, a novel WiFi-based gesture recognition system that introduces WiFi latent domain mining to redefine domains directly from the data itself. GesFi first processes raw sensing data collected from WiFi receivers using CSI-ratio denoising, Short-Time Fast Fourier Transform, and visualization techniques to generate standardized input representations. It then employs class-wise adversarial learning to suppress gesture semantic and leverages unsupervised clustering to automatically uncover latent domain factors responsible for distributional shifts. These latent domains are then aligned through adversarial learning to support robust cross-domain generalization. Finally, the system is applied to the target environment for robust gesture inference. We deployed GesFi under both single-pair and multi-pair settings using commodity WiFi transceivers, and evaluated it across multiple public datasets and real-world environments. Compared to state-of-the-art baselines, GesFi achieves up to 78% and 50% performance improvements over existing adversarial methods, and consistently outperforms prior generalization approaches across most cross-domain tasks.

翻译：本文提出GesFi，一种新颖的基于WiFi的手势识别系统，该系统引入WiFi潜在域挖掘技术，直接从数据本身重新定义领域。GesFi首先对WiFi接收器采集的原始传感数据，采用CSI-ratio去噪、短时快速傅里叶变换及可视化技术进行处理，生成标准化的输入表征。随后，系统利用类间对抗学习抑制手势语义，并借助无监督聚类自动揭示导致分布偏移的潜在域因素。这些潜在域通过对抗学习进行对齐，以支持鲁棒的跨域泛化。最终，系统应用于目标环境以实现鲁棒的手势推断。我们在商用WiFi收发器上，分别于单收发对及多收发对配置下部署了GesFi，并在多个公共数据集和真实环境中对其进行了评估。与现有最先进的基线方法相比，GesFi相较于现有对抗方法实现了最高78%和50%的性能提升，并且在大多数跨域任务中持续优于先前的泛化方法。