Learning Human Activities through Wi-Fi Channel State Information with Multiple Access Points

Abstract

Wi-Fi channel state information (CSI) provides adequate information for recognizing and analyzing human activities. Because of the short distance and low transmit power of Wi-Fi communications, people usually deploy multiple access points (APs) in a small area. Traditional Wi-Fi CSI based human activity recognition methods adopt Wi-Fi CSI from a single AP, which is not so appropriate for a high-density Wi-Fi environment. In this paper, we propose a learning method that analyzes the CSI of multiple APs in a small area to detect and recognize human activities. We introduce a deep learning model to process complex and large CSI information from multiple APs. From extensive experiment results, our method performs better than other solutions in a given environment where multiple Wi-Fi APs exist.