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Learning Human Activities through Wi-Fi Channel State Information with Multiple Access Points.
- Source :
-
IEEE Communications Magazine . May2018, Vol. 56 Issue 5, p124-129. 6p. - Publication Year :
- 2018
-
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 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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 01636804
- Volume :
- 56
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Communications Magazine
- Publication Type :
- Academic Journal
- Accession number :
- 129761806
- Full Text :
- https://doi.org/10.1109/MCOM.2018.1700083