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传感技术的不断进步带来了许多普及应用,例如家庭监控、远程医疗、道路安全以及家庭娱乐等。然而,现有文献多是针对单一主体进行活动分类。与此相对,实际场景下需要对多个主体同时活动进行分类,这是一项更为实际的挑战。同时,随着主体和环境的变化,Wi-Fi传感系统很难对环境进行适应。本文提出了SiMWiSense框架,该框架是第一个基于Wi-Fi技术的多人同时活动分类算法。为了解决分类中主体和活动数量呈指数上升的挑战,我们提出使用距离人体最近的设备计算信道状态信息(CSI)的方法。通过实验证明了这种方式的正确性,更好的分类效果也得到了验证。为了解决系统泛化上的问题,我们开发了全新的元学习算法,名为特征可重复嵌入学习(FREL) 。基于 3 种不同环境下,3 个主体同时进行 20 个不同活动的全面数据采集,我们证明了 SiMWiSense 的分类准确度高达 97% ,而 FREL 在仅仅使用每个类别 15 秒的额外数据的情况下,相对传统的卷积神经网络 (CNN) 针对元学习的最新算法 (FSEL) 同时提高了 85% 和 20% 的准确度。为了重现研究结果,我们提供了 1TB 的数据集和代码仓库。