Learning Human Activities through Wi Fi Channel State Information with Multiple Access Points
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 very appropriate for a high density Wi Fi environment. In this article, 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 from multiple APs. From extensive experiment results, our method performs better than other solutions in a given environment where multiple Wi Fi APs exist.
IEEE Communications Magazine