发表机构
National Mobile Communications Research Laboratory, Southeast University（东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对WiFi多用户活动识别中信号叠加与用户关联难题，提出UCSA模块，通过FiLM条件化空间注意力生成用户专属注意力图，结合SMSA，在WiMANS基准上以超93%平均准确率超越九个基线。
AI 中文摘要
基于WiFi的人体活动识别在单用户场景下已实现高精度。然而，识别多个并发用户执行的活动仍具挑战性，因为他们的身体反射传播路径在信道状态信息（CSI）测量中相互叠加。现有的多用户方法要么在分类前分解信号，导致误差传播，要么联合预测活动而无法可靠地将每个预测与正确的用户关联。此外，已被证明对单用户感知有效的注意力机制计算单一的用户无关注意力图，混合了不同用户的模式，且没有机制将特征与特定用户槽关联。本文提出一种用户条件空间注意力（UCSA）模块，通过特征级线性调制（FiLM）将多尺度空间注意力以可学习的用户ID嵌入为条件，生成每个用户的空间注意力图。UCSA在空间特征增强与用户区分之间建立了深度耦合：注意力模块本身变得用户感知，为每个用户槽生成不同的注意力图，而非依赖后期融合的嵌入加法。结合用于多尺度空间增强的可共享多语义空间注意力（SMSA）模块，所提出的框架在统一架构中同时解决了特征质量和用户区分问题。在WiMANS基准上，跨三个室内环境和最多五个并发用户的实验表明，所提方法在所有设置中均优于九个基线，即使在五个用户的情况下，三个环境的平均准确率也超过93%。消融研究证实，SMSA和UCSA均对识别准确率有显著贡献，其中UCSA提供了共享特征与每个用户预测之间的关键联系。
英文摘要
WiFi-based human activity recognition has achieved high accuracy under the single-user scene. Recognizing activities performed by multiple concurrent users remains challenging, because their body-reflected propagation paths superimpose in the channel state information (CSI) measurement. Existing multi-user methods either decompose the signal before classification, suffering from error propagation, or predict activities jointly without reliably associating each prediction with the correct user. Moreover, attention mechanisms that have proven effective for single-user sensing compute a single user-agnostic attention map, blending patterns from different users and providing no mechanism to associate features with specific user slots. This paper proposes a user-conditioned spatial attention (UCSA) module that generates per-user spatial attention maps by conditioning multi-scale spatial attention on learnable user-ID embeddings via feature-wise linear modulation (FiLM). UCSA creates a deep coupling between spatial feature enhancement and user differentiation: the attention module itself becomes user-aware, producing distinct attention maps for each user slot rather than relying on a late-fusion embedding addition. Combined with a shareable multi-semantic spatial attention (SMSA) module for multi-scale spatial enhancement, the proposed framework addresses both feature quality and user differentiation in a unified architecture. Experiments on the WiMANS benchmark across three indoor environments and up to five concurrent users show that the proposed method outperforms nine baselines in all settings, achieving an average accuracy of over 93\% across three environments even with five users. Ablation studies confirm that both SMSA and UCSA contribute substantially to recognition accuracy, with UCSA providing the critical link between shared features and per-user predictions.
