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时间:2016/12/27 16:23:37 点击数:

报告题目Human Activity Detection based on WiFi Signal in Sensorless Environments


报告人 刘永和教授 (University of Texas at Arlington)

报告时间20161230 10:00-11:00



An extensive set of research efforts have explored Channel State Information (CSI) for human activity detection. By extracting CSI from a sequence of packets, one can statistically analyze the temporal variations embedded therein and recognize corresponding human activities. This way, potential challenges of deploying conventional sensor systems will be avoided. In this talk, we will provide an overview of human activity recognition system based on CSI from ubiquitous WiFi packets. We will also present a system termed Wi-Chase developed in our lab. Different from existing schemes utilizing only CSI of one or a small subset of subcarriers, Wi-Chase fully utilizes all available subcarriers of the WiFi signal and incorporates variations in both their phases and magnitudes. As each subcarrier carries integral information that will improve the recognition accuracy because of detailed correlated information content in different subcarriers, we can achieve much higher detection accuracy. Our experimental results show that Wi-Chase is robust and achieves an average classification accuracy greater than 97% for multiple communication links. Wi-Chase has an average accuracy greater than 90% for all the parametric variations we tested in multiple experiments.


Yonghe Liu is an associate professor at the Department of Computer Science and Engineering, the University of Texas at Arlington. He obtained the B.S. and M.S. degree from Tsinghua University in 1998 and 1999 respectively, and the Ph.D. degree from Rice University in 2004. His research interests are wireless systems and applications, Internet of things, and security.