From Signal to Image: Capturing Fine-Grained Human Poses With Commodity Wi-Fi

Abstract

Human sensing based on commodity Wi-Fi devices has become a promising technique in human tracking, gesture recognition, walking speed monitoring, in-home healthcare, etc. However, past human sensing systems usingWi-Fi capture limited information about humans. Hence in this letter, we try to make commodity Wi-Fi devices act as cameras to directly capture human poses, i.e., fine-grained human skeleton images. We use a synchronized camera to capture human skeletons as annotations for Wi-Fi signals and design a novel neural network to convert Wi-Fi signals into images. We utilize three transceivers coordinately and use amplitude and phase information of Channel State Information (CSI) jointly to improve the resolution of Wi-Fi signals. We also introduce a method to extract useful and accurate CSI corresponding to humans and construct CSI images which are input of the neural network. Experimental results show that commodity Wi-Fi devices can capture human poses almost as fine-grained as cameras.