Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities. Moreover, it is known that Wi-Fi sensing systems struggle to adapt to new environments and subjects. To address both issues, we propose SiMWiSense, the first framework for simultaneous multi-subject activity classification based on Wi-Fi that generalizes to multiple environments and subjects. We address the scalability issue by using the Channel State Information (CSI) computed from the device positioned closest to the subject. We experimentally prove this intuition by confirming that the best accuracy is experienced when the CSI computed by the transceiver positioned closest to the subject is used for classification. To address the generalization issue, we develop a brand-new few-shot learning algorithm named Feature Reusable Embedding Learning (FREL). Through an extensive data collection campaign in 3 different environments and 3 subjects performing 20 different activities simultaneously, we demonstrate that SiMWiSense achieves classification accuracy of up to 97%, while FREL improves the accuracy by 85% in comparison to a traditional Convolutional Neural Network (CNN) and up to 20% when compared to the state-of-the-art few-shot embedding learning (FSEL), by using only 15 seconds of additional data for each class. For reproducibility purposes, we share our 1TB dataset and code repository.

翻译：近期，Wi-Fi感知技术的进步催生了众多普适计算应用，涵盖家居监控、远程医疗、道路安全及家庭娱乐等领域。现有研究大多局限于单一人体目标在特定时刻的活动分类，而更具现实意义的场景是实现多目标同步活动分类。该领域面临两大核心挑战：其一，活动类别数量随目标数与活动种类呈指数级增长；其二，Wi-Fi感知系统难以适应新环境与新目标。针对这两大问题，我们提出了SiMWiSense——首个基于Wi-Fi的多目标同步活动分类框架，能够泛化至多环境与多目标场景。通过采用距离目标最近的设备采集的信道状态信息（CSI），我们有效解决了可扩展性问题。实验验证了该直觉：使用距离目标最近的收发设备获取的CSI进行分类时，精度达到最优。为攻克泛化难题，我们创新性地开发了特征复用嵌入学习（FREL）这种新型小样本学习算法。通过在3种不同环境中开展大规模数据采集实验（3名目标同步执行20种活动），我们证明SiMWiSense的分类准确率高达97%，而FREL仅需每类别15秒的额外数据即可在传统卷积神经网络（CNN）基础上提升85%准确率，相较于当前最优的小样本嵌入学习（FSEL）方法也有20%的提升。为保障研究可复现性，我们公开了1TB数据集与代码仓库。