4.6 Article

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

期刊

SENSORS
卷 19, 期 6, 页码 -

出版社

MDPI
DOI: 10.3390/s19061421

关键词

duration estimation; human motion detection; channel statement information; back propagation neural network; WiFi

资金

  1. National Key Research and Development Project of China [2018YFF0301004]
  2. National Natural Science Foundation of China [61802107]
  3. Natural Science Foundation of Hebei Province of China [F2018402251]
  4. Handan Science and Technology Research and Development Program [1721203048]
  5. Open Project of Hebei Internet of Things Data Acquisition and Processing Engineering Research Center [2016-2]
  6. Science and Technology Research Key Program in Higher Education of Hebei Province of China [ZD2016017]

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据