A Framework for Human Activity Recognition Based on WiFi CSI Signal Enhancement

With the advancement of wireless technologies and sensing methodologies, many studies have shown that wireless signals can sense human behaviors. Human activity recognition using channel state information (CSI) in commercial WiFi devices plays an important role in many applications. In this paper, a framework for human activity recognition was constructed based on WiFi CSI signal enhancement. Firstly, the sensitivity of different antennas to human activity was studied. An antenna selection algorithm was proposed, which can make a choice of the antenna automatically based on their sensitivity in accordance with different activities. Secondly, two signal enhancement approaches, which can strengthen the active signals and weaken the inactive signals, were proposed to extract the active interval caused by human activity. Finally, an activity segmentation algorithm was proposed to detect the start and end time of activity. In order to verify and evaluate the methods, extensive experiments have been conducted in real indoor environments. The experimental results have demonstrated that our solutions can eliminate a large number of redundant information brought by insensitive and inactive signals. Our research results can be put into use to improve recognition accuracy significantly and decrease the cost of recognition time.