Wi-CR: Human Action Counting and Recognition with Wi-Fi Signals

Abstract

Human continuous activity recognition, i.e. automatic inference of human behavior, plays an increasingly important role in many fields, such as smart home, somatic games, and health care. The widening application of wireless technology in sensing is making human continuous activity recognition more unobtrusive and user-friendly. In this paper, we propose a Channel State Information (CSI) based human action counting and recognition method, which is named Wi-CR. Wi-CRtakes advantage of an activity indicator and a threshold to detect the start and end times of a set of continuous actions, then counts the number of actions through a peak-finding algorithm, and determines the start and end times of each action. After that, Wi-CRemploys Discrete Wavelet Transformation (DWT) to extract features to analyze correlation of action waveforms and perform best-fit matching based on dynamic time warping (DTW). Finally, it recognizes the action of each action period by k-Nearest Neighbors (KNN). The experimental results show that Wi-CRcan achieve action counting accuracy of 95% and recognition accuracy of 90%, in the scenarios with two types of actions (squat and walk) occurring simultaneously.