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R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi

Authors :
Jijun Zhao
Lishuang Liu
Zhongcheng Wei
Chunhua Zhang
Wei Wang
Yongjian Fan
Source :
Sensors, Vol 19, Iss 6, p 1421 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

Abstract

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.

Details

Language :
English
ISSN :
14248220
Volume :
19
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.5cae8d96555342d7a89f0fc31b65d7b2
Document Type :
article
Full Text :
https://doi.org/10.3390/s19061421