@article{oai:muroran-it.repo.nii.ac.jp:00009966,
 author = {LI, He and 李, 鶴 and OTA,  Kaoru and 太田, 香 and DONG,  Mianxiong and 董, 冕雄 and GUO, Minyi},
 issue = {5},
 journal = {IEEE Communications Magazine},
 month = {May},
 note = {application/pdf, 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.},
 pages = {124--129},
 title = {Learning Human Activities through Wi-Fi Channel State Information with Multiple Access Points},
 volume = {56},
 year = {2018},
 yomi = {リ, ホ and オオタ, カオル and トウ, メンユウ}
}