Fine-Grained Adaptive Location-Independent Activity Recognition Using Commodity Wifi
2018 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)(2018)
摘要
Device-free activity recognition is appealing in smart home applications. It not only is convenient, but also causes no privacy concern, as compared to other activity recognition techniques such as the vision based technique. Existing WiFi-based methods have achieved high accuracy in static circumstances but have limitations in adapting changes in environment and activities locations. In this paper, we propose a fine-grained adaptive location-independent activity recognition system (FALAR) which leverages WiFi signals to characterize and recognize common activities regardless of inconsistency of mutative surroundings. FALAR applies fine-grained channel state information (CSI) to achieve accurate recognitions. To address the issue of environmental changes, we present a Kernel Density Estimation (KDE) based motion extraction method and a coarse-to-fine search strategy for speedy processing. After a denoising scheme, we introduce Class Estimated Basis Space Singular Value Decomposition (CSVD) to efface the static path in the background, and use nonnegative matrix factorization to distinguish various activities by looking into the signal profiles. We evaluate FALAR using two commodity WiFi routers in a typical office environment. Our results show that it achieves remarkable performance.
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关键词
vision based technique,fine-grained adaptive location-independent activity recognition system,FALAR,WiFi signals,fine-grained channel state information,Kernel Density Estimation based motion extraction method,Class Estimated Basis Space Singular Value Decomposition,smart home applications,activity recognition techniques,WiFi routers
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