To reuse the densely deployed WiFi devices which are currently for communication, we present WiLink, the first 3D human pose estimation system for the large sensing range. Mainly contributions are as follows:
1、We propose a method to identify and remove bad WiFi links.
2、We propose indicators to measure the importance of links and the redundancy between links.
3、We propose an algorithm (DLS) based on maximum weights and minimum redundancy to dynamically select several effective WiFi links instead of all links.
4、We input CSI data of the selected links into neural network to extract features related to human pose, and convert these features into key point coordinates.
Experimental results show that compared with all WiFi links, using the links selected by the DLS algorithm as input of the neural network can enhance the estimation accuracy and reduce the computing time.