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

作者：Li, He^*; Ota, Kaoru; Dong, Mianxiong; Guo, Minyi

来源：IEEE Communications Magazine, 2018, 56(5): 124-129.

DOI：10.1109/MCOM.2018.1700083

• Human activities
• Human activity recognition
• Learning methods
• Learning models
• Multiple access points
• Multiple aps
• Transmit power
• Wi-Fi communications

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.

• 出版日期2018-5
• 单位上海交通大学