提出轻量级注意力网络，提升无线手势识别的精度与可解释性。
Variational Dual-path Attention Network for CSI-Based Gesture Recognition
- 分频域滤波与时域检测双路径处理，结构化优化特征
- 引入变分推断建模注意力不确定性，噪声下准确率提升12.3%
- 设计可解释性强，适合边缘设备部署的无线感知系统
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。现有端到端模型将特征提取与分类紧密耦合，忽视了CSI固有的时频稀疏性，导致冗余并影响泛化能力。为此，本文提出一种轻量级前端预处理模块——变分双路径注意力网络（VDAN），通过频域滤波与时域检测实现结构化特征精炼。引入变分推断建模注意力权重的不确定性，增强对噪声的鲁棒性。从信息瓶颈与正则化角度阐释模块设计原理。在公开数据集上的实验表明，学习到的注意力权重与CSI的物理稀疏特性一致，验证了其可解释性。该工作为资源受限的无线感知系统提供了高效且可解释的前端处理方案。
原文摘要 · Abstract (English)
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.
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