Chowdhury, T.Z., Leung, C. and Miao, C.Y. (2017) WiHACS: Leveraging WiFi for Human Activity Classification Using OFDM Subcarriers’ Correlation. Proceeding of the 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, 14-16 November 2017, 338-342.
https://doi.org/10.1109/GlobalSIP.2017.8308660

has been cited by the following article:

• TITLE: Crowd Counting Based on WiFi Channel State Information and Transfer Learning

  AUTHORS: Zhongqiang Wu, Pengyu Ji, Manxi Ma, Wencheng Zhuang, Zhenyu Li, Jihao Cui, Zhengjie Wang

  KEYWORDS: Crowd Counting, Channel Status Information, Transfer Learning, ResNet

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.6, June 15, 2022

  ABSTRACT: With the popularity and development of indoor WiFi equipment, they have more sensing capability and can be used as a human monitoring device. We can collect the channel state information (CSI) from WiFi device and acquire the human state based on the measurements. These studies have attracted wide attention and become a hot research topic. This paper concentrated on the crowd counting based on CSI and transfer learning. We utilized the CSI signal fluctuations caused by human motion in WiFi coverage to identify the person count because different person counts would lead to unique signal propagation characteristics. First, this paper presented recent studies of crowd counting based on CSI. Then, we introduced the basic concept of CSI, and described the fundamental principle of CSI-based crowd counting. We also presented the system framework, experiment scenario, and neural network structure transferred from the ResNet. Next, we presented the experiment results and compared the accuracy using different neural network models. The system achieved recognition accuracy of this 100 percent for seven participants using the transfer learning technique. Finally, we concluded the paper by discussing the current problems and future work.