@inproceedings{e3254f3fa7574fcd9de1f155df55e63f,
title = "Human Activities Recognition with Amplitude-Phase of Channel State Information using Deep Residual Networks",
abstract = "To address the issue of low accuracy in existing activities recognition schemes with channel state information (CSI), a new scheme is proposed based on collecting CSI time series data and building a deep residual systolic neural network (DRSN) for end-to-end supervised learning feature extraction, which takes into account the amplitude and phase changes of CSI, to achieve high accuracy in activity recognition. The results show that the amplitude and phase analysis based on CSI data in the deep residual network can achieve good activity recognition with an average accuracy of 97.2%.",
keywords = "activity recognition, amplitude and phase, CSI, DRSN",
author = "Xing Ming and Wei Cheng and Zhu, {Ruo Lan} and Yue Zhou and Liu, {Xiao Rui} and Yan, {Bei Ming} and Lei Zhu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022 ; Conference date: 16-12-2022 Through 19-12-2022",
year = "2022",
doi = "10.1109/ICIEA54703.2022.10006309",
language = "英语",
series = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1183--1188",
editor = "Wenxiang Xie and Shibin Gao and Xiaoqiong He and Xing Zhu and Jingjing Huang and Weirong Chen and Lei Ma and Haiyan Shu and Wenping Cao and Lijun Jiang and Zeliang Shu",
booktitle = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",
}