WiFi Action Recognition via Vision-based Methods

Action recognition via WiFi has caught intense attention recently because of its ubiquity, low cost, and privacy-preserving. Observing Channel State Information (CSI, a fine-grained information computed from the received WiFi signal) resemblance to texture, we transform the received CSI into images, extract features with vision-based methods and train SVM classifiers for action recognition. Our experiments show that regarding CSI as images achieves an accuracy above 85% classifying 7 actions. However, from the experimental results, the CSI is usually location dependent, which affects the recognition performance if signals are recorded in different places. In this work, 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 CSI streams could achieve promising accuracy above 90% in identifying six actions even testing in five different rooms. Our contributions include: To our best knowledge, we are the first to investigate the feasibility of processing CSI by vision-based methods with extendable learning-based framework. We regard CSI of each Tx-Rx pair as a channel and investigate early and late fusion of multi-channels. Also, we achieve promising accuracy on action recognition on a specific location. We enable cross-room action recognition with the proposed location infomation removal method.