Location-Independent WiFi Action Recognition via Vision-based Methods

Abstract

Due to die characteristics of ubiquity, non occlusion,privacy preservation of Win, many researchers have devoted to human action recognition using WiFi signals. As demonstrated in [1], Channel State information (CSI), a fine-grained information capturing the properties of WiFi signal propagation, could be transformed into images for achieving a promising accuracy on action recognition via vision-based :methods. However, from the experimental results shown in [1], the CSI is usually location dependent, which affects the recognition performance if signals are recorded in different places. In this paper. We propose a location-dependency removal method based on Singular Value Decomposition (SVD) to eliminate the background CSI and effectively extract the channel information of signals reflected by human bodies. Experimental results show that our method considering the correlation of CST streams could achieve promising accuracy above 90% in identifying six actions even testing in live different rooms.