Wi-Fi gesture recognition based on Channel State Information (CSI) is challenged by high-dimensional noise and resource constraints on edge devices. Prevailing end-to-end models tightly couple feature extraction with classification, overlooking the inherent time-frequency sparsity of CSI and leading to redundancy and poor generalization. To address this, this paper proposes a lightweight feature preprocessing module--the Variational Dual-path Attention Network (VDAN). It performs structured feature refinement through frequency-domain filtering and temporal detection. Variational inference is introduced to model the uncertainty in attention weights, thereby enhancing robustness to noise. The design principles of the module are explained from the perspectives of the information bottleneck and regularization. Experiments on a public dataset demonstrate that the learned attention weights align with the physical sparse characteristics of CSI, verifying its interpretability. This work provides an efficient and explainable front-end processing solution for resource-constrained wireless sensing systems.

翻译：基于信道状态信息(CSI)的Wi-Fi手势识别面临高维噪声和边缘设备资源受限的挑战。主流端到端模型将特征提取与分类紧密耦合，忽视了CSI固有的时频稀疏特性，导致冗余和泛化能力差。为此，本文提出一种轻量级特征预处理模块——变分双通路注意力网络(VDAN)。该模块通过频域滤波与时域检测实现结构化特征优化，并引入变分推理对注意力权重的不确定性进行建模，从而增强对噪声的鲁棒性。本文从信息瓶颈与正则化角度阐释了模块的设计原理。在公开数据集上的实验表明，学习到的注意力权重与CSI的物理稀疏特性相符，验证了其可解释性。本研究为资源受限的无线感知系统提供了一种高效且可解释的前端处理方案。