Human Activities Recognition with Amplitude-Phase of Channel State Information using Deep Residual Networks

Xing Ming; Wei Cheng; Ruo Lan Zhu; Yue Zhou; Xiao Rui Liu; Bei Ming Yan; Lei Zhu

doi:10.1109/ICIEA54703.2022.10006309

Human Activities Recognition with Amplitude-Phase of Channel State Information using Deep Residual Networks

Xing Ming, Wei Cheng, Ruo Lan Zhu, Yue Zhou, Xiao Rui Liu, Bei Ming Yan, Lei Zhu

School of Electronics and Information

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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%.

Original language	English
Title of host publication	ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications
Editors	Wenxiang Xie, Shibin Gao, Xiaoqiong He, Xing Zhu, Jingjing Huang, Weirong Chen, Lei Ma, Haiyan Shu, Wenping Cao, Lijun Jiang, Zeliang Shu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1183-1188
Number of pages	6
ISBN (Electronic)	9781665409841
DOIs	https://doi.org/10.1109/ICIEA54703.2022.10006309
State	Published - 2022
Event	17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022 - Chengdu, China Duration: 16 Dec 2022 → 19 Dec 2022

Publication series

Name	ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications

Conference

Conference	17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022
Country/Territory	China
City	Chengdu
Period	16/12/22 → 19/12/22

Keywords

activity recognition
amplitude and phase
CSI
DRSN

Access to Document

10.1109/ICIEA54703.2022.10006309

Cite this

Ming, X., Cheng, W., Zhu, R. L., Zhou, Y., Liu, X. R., Yan, B. M., & Zhu, L. (2022). Human Activities Recognition with Amplitude-Phase of Channel State Information using Deep Residual Networks. In W. Xie, S. Gao, X. He, X. Zhu, J. Huang, W. Chen, L. Ma, H. Shu, W. Cao, L. Jiang, & Z. Shu (Eds.), ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications (pp. 1183-1188). (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA54703.2022.10006309

Ming, Xing ; Cheng, Wei ; Zhu, Ruo Lan et al. / Human Activities Recognition with Amplitude-Phase of Channel State Information using Deep Residual Networks. ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. editor / Wenxiang Xie ; Shibin Gao ; Xiaoqiong He ; Xing Zhu ; Jingjing Huang ; Weirong Chen ; Lei Ma ; Haiyan Shu ; Wenping Cao ; Lijun Jiang ; Zeliang Shu. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1183-1188 (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications).

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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",

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Ming, X, Cheng, W, Zhu, RL, Zhou, Y, Liu, XR, Yan, BM & Zhu, L 2022, Human Activities Recognition with Amplitude-Phase of Channel State Information using Deep Residual Networks. in W Xie, S Gao, X He, X Zhu, J Huang, W Chen, L Ma, H Shu, W Cao, L Jiang & Z Shu (eds), ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications, Institute of Electrical and Electronics Engineers Inc., pp. 1183-1188, 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022, Chengdu, China, 16/12/22. https://doi.org/10.1109/ICIEA54703.2022.10006309

Human Activities Recognition with Amplitude-Phase of Channel State Information using Deep Residual Networks. / Ming, Xing; Cheng, Wei; Zhu, Ruo Lan et al.
ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. ed. / Wenxiang Xie; Shibin Gao; Xiaoqiong He; Xing Zhu; Jingjing Huang; Weirong Chen; Lei Ma; Haiyan Shu; Wenping Cao; Lijun Jiang; Zeliang Shu. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1183-1188 (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Human Activities Recognition with Amplitude-Phase of Channel State Information using Deep Residual Networks

AU - Ming, Xing

AU - Cheng, Wei

AU - Zhu, Ruo Lan

AU - Zhou, Yue

AU - Liu, Xiao Rui

AU - Yan, Bei Ming

AU - Zhu, Lei

PY - 2022

Y1 - 2022

N2 - 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%.

AB - 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%.

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KW - amplitude and phase

KW - CSI

KW - DRSN

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AN - SCOPUS:85146835916

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SP - 1183

EP - 1188

BT - ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications

A2 - Xie, Wenxiang

A2 - Gao, Shibin

A2 - He, Xiaoqiong

A2 - Zhu, Xing

A2 - Huang, Jingjing

A2 - Chen, Weirong

A2 - Ma, Lei

A2 - Shu, Haiyan

A2 - Cao, Wenping

A2 - Jiang, Lijun

A2 - Shu, Zeliang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022

Y2 - 16 December 2022 through 19 December 2022

ER -

Ming X, Cheng W, Zhu RL, Zhou Y, Liu XR, Yan BM et al. Human Activities Recognition with Amplitude-Phase of Channel State Information using Deep Residual Networks. In Xie W, Gao S, He X, Zhu X, Huang J, Chen W, Ma L, Shu H, Cao W, Jiang L, Shu Z, editors, ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1183-1188. (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications). doi: 10.1109/ICIEA54703.2022.10006309

Human Activities Recognition with Amplitude-Phase of Channel State Information using Deep Residual Networks

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