A Doppler-based Human Activity Recognition System using WiFi Signals

WiFi-based human activity recognition has drawn a lot of attention in recent years due to the low cost and high popularity of WiFi devices. The wireless monitoring system is able to efficiently detect abnormal activities like falling and body shaking, without privacy invasion. In this paper, we propose a framework using Doppler Frequency Shift-based methodology to extract the features and classify different activities with channel state information collected from WiFi devices. The experimental results demonstrate the reliability of our method for the application of activity recognition.