LAUSR

With the proliferation of WiFi devices and infrastructures, the ubiquitous WiFi signals are used to transmit user data. Besides it is also capable of sensing and identifying human gestures. In this paper, we propose a WiFi-based gesture recognition system, namely WiGrus, which solves the problems of user privacy and energy consumption compared with the approaches using wearable sensors and depth cameras. WiGrus leverages the fine-grained Channel State Information (CSI) extracted from WiFi signals to recognize a set of hand gestures. First of all, we utilize timestamps attached to the extracted CSI values to split continuously received WiFi packets into gesture instances. Second, a Principal Component Analysis (PCA)-based method and the first order difference are employed to reduce the noise and mitigate multipath effects caused by the environment changes. Then, massive features are extracted from the processed CSI values to present the intrinsic characteristics of each gesture. Finally, a 2-stage-RF algorithm is proposed to classify the gestures. Our experiments are implemented with a wireless router and a Software Defined Radio (SDR) device, more specifically Universal Software Radio Peripheral (USRP), which are used as WiFi signal transmitter and receiver respectively. The experimental results demonstrate that WiGrus can achieve an average accuracy of 96% in Line-of-sight (LOS) scenario and 92% in Non-Line-of-Sight (NLOS) scenario in the office environment and is robust to the environment changes.