Wi-Fi-based human activity recognition (HAR) provides substantial convenience and has emerged as a thriving research field, yet the coarse spatial resolution inherent to Wi-Fi significantly hinders its ability to distinguish multiple subjects. By exploiting the near-field domination effect, establishing a dedicated sensing link for each subject through their personal Wi-Fi device offers a promising solution for multi-person HAR under native traffic. However, due to the subject-specific characteristics and irregular patterns of near-field signals, HAR neural network models require fine-tuning (FT) for cross-domain adaptation, which becomes particularly challenging with certain categories unavailable. In this paper, we propose WiAnchor, a novel training framework for efficient cross-domain adaptation in the presence of incomplete activity categories. This framework processes Wi-Fi signals embedded with irregular time information in three steps: during pre-training, we enlarge inter-class feature margins to enhance the separability of activities; in the FT stage, we innovate an anchor matching mechanism for cross-domain adaptation, filtering subject-specific interference informed by incomplete activity categories, rather than attempting to extract complete features from them; finally, the recognition of input samples is further improved based on their feature-level similarity with anchors. We construct a comprehensive dataset to thoroughly evaluate WiAnchor, achieving over 90% cross-domain accuracy with absent activity categories.

翻译：基于Wi-Fi的人体活动识别（HAR）提供了极大便利并已成为一个蓬勃发展的研究领域，然而Wi-Fi固有的低空间分辨率严重制约了其区分多个目标的能力。通过利用近场主导效应，借助每个受试者的个人Wi-Fi设备为其建立专属传感链路，为原生流量下的多人HAR提供了有前景的解决方案。然而，由于近场信号的受试者特异性特征和不规则模式，HAR神经网络模型需要微调（FT）以实现跨域适应，当某些活动类别缺失时这一过程尤为困难。本文提出WiAnchor——一种面向不完整活动类别的高效跨域适应新型训练框架。该框架通过三步处理嵌入不规则时间信息的Wi-Fi信号：在预训练阶段，我们扩大类间特征边界以增强活动可分离性；在微调阶段，我们创新性地引入锚点匹配机制实现跨域适应，通过不完整活动类别提供的信息过滤受试者特异性干扰，而非试图从中提取完整特征；最后，基于输入样本与锚点的特征级相似性进一步改进识别效果。我们构建了综合性数据集以全面评估WiAnchor，在活动类别缺失情况下实现了超过90%的跨域准确率。