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R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi
As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion le...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6471411/ https://www.ncbi.nlm.nih.gov/pubmed/30909467 http://dx.doi.org/10.3390/s19061421 |
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author | Zhao, Jijun Liu, Lishuang Wei, Zhongcheng Zhang, Chunhua Wang, Wei Fan, Yongjian |
author_facet | Zhao, Jijun Liu, Lishuang Wei, Zhongcheng Zhang, Chunhua Wang, Wei Fan, Yongjian |
author_sort | Zhao, Jijun |
collection | PubMed |
description | As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively. |
format | Online Article Text |
id | pubmed-6471411 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64714112019-04-26 R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi Zhao, Jijun Liu, Lishuang Wei, Zhongcheng Zhang, Chunhua Wang, Wei Fan, Yongjian Sensors (Basel) Article As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively. MDPI 2019-03-22 /pmc/articles/PMC6471411/ /pubmed/30909467 http://dx.doi.org/10.3390/s19061421 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhao, Jijun Liu, Lishuang Wei, Zhongcheng Zhang, Chunhua Wang, Wei Fan, Yongjian R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi |
title | R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi |
title_full | R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi |
title_fullStr | R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi |
title_full_unstemmed | R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi |
title_short | R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi |
title_sort | r-dehm: csi-based robust duration estimation of human motion with wifi |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6471411/ https://www.ncbi.nlm.nih.gov/pubmed/30909467 http://dx.doi.org/10.3390/s19061421 |
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