Chang J.-Y.Lee K.-Y.Lin K.C.-J.Hsu W.2019-07-102019-07-102016978147999988015206149https://scholars.lib.ntu.edu.tw/handle/123456789/413025Action 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%. 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. ? We could know where and what action user performs with location-awareness classification. ? 2016 IEEE.action recognition; texture; vision-based; WiFi[SDGs]SDG16conference paper10.1109/ICASSP.2016.74721842-s2.0-84973345480