SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals

作者：Khandaker Foysal Haque, Milin Zhang, Francesco Restuccia · 年份：2023 · DOI：10.1109/wowmom57956.2023.00019 · 被引用次数：9 · 研究领域：Indoor and Outdoor Localization Technologies、Context-Aware Activity Recognition Systems、Gait Recognition and Analysis

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...