用无线信号自动生成隐含域，提升手势识别跨环境稳定性
Beyond Physical Labels: Redefining Domains for Robust WiFi-based Gesture Recognition
- 从原始信号中挖掘隐含域，无需人工标注物理标签
- 在多场景下相比顶尖方法性能提升最高达78%
- 适合需要低部署成本的智能家居与无障碍交互场景
本文提出GesFi，一种基于WiFi的手势识别系统，通过无线信号潜在域挖掘重新定义领域。系统首先对采集的原始传感数据进行信道状态信息比值去噪、短时快速傅里叶变换及可视化处理，生成标准化输入表示；随后采用类别级对抗学习抑制手势语义干扰，并利用无监督聚类自动发现导致分布偏移的潜在域因素；这些潜在域通过对抗学习对齐，以支持鲁棒的跨域泛化。最终系统在目标环境中实现稳定手势推断。我们在单对与多对设置下使用商用WiFi收发器部署GesFi，评估了多个公开数据集和真实环境。相比现有最先进基线，GesFi在跨域任务上性能较对抗方法最高提升78%，较以往泛化方法持续领先。
原文摘要 · Abstract (English)
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.
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