Device-free human micro-activity recognition method using WiFi signals

Human activity tracking plays a vital role in human–computer interaction. Traditional human activity recognition (HAR) methods adopt special devices, such as cameras and sensors, to track both macro- and micro-activities. Recently, wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment. This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information (CSI) of wireless signals. Different from existed CSI-based micro-activity recognition methods, the proposed method extracts both amplitude and phase information from CSI, thereby providing more information and increasing detection accuracy. The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity. We applied a machine learning algorithm to recognize the proposed micro-activities. The proposed method has been evaluated in both line of sight (LOS) and none line of sight (NLOS) scenarios, and the empirical results demonstrate the effectiveness of the proposed method with several users.