CSI-based underground personnel behavior identification method

Lei ZHANG; Yue ZHANG; Mingxue LI; Xinguo SHI; Bo ZHAI; Weilong WANG

doi:10.11959/j.issn.2096-3750.2020.00202

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CSI-based underground personnel behavior identification method

Topic:IoT+Energy | 更新时间：2024-06-05

- CSI-based underground personnel behavior identification method
- Chinese Journal on Internet of Things Vol. 4, Issue 4, Pages: 26-31(2020)
- 作者机构：
  
  1. 徐州工程学院信息工程学院（大数据学院），江苏徐州 221000
  2. 中国矿业大学物联网（感知矿山）研究中心，江苏徐州 221000
  3. 中国矿业大学电气与动力工程学院，江苏徐州 221000
  4. 山东能源淄博矿业集团有限公司信息中心，山东淄博 225100
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2020.00202
  CLC： TD76
- Published：30 December 2020，
  
  Published Online：2020-12，
- 稿件说明：
移动端阅览
LEI ZHANG, YUE ZHANG, MINGXUE LI, et al. CSI-based underground personnel behavior identification method. [J]. Chinese journal on internet of things, 2020, 4(4): 26-31.
DOI：

LEI ZHANG, YUE ZHANG, MINGXUE LI, et al. CSI-based underground personnel behavior identification method. [J]. Chinese journal on internet of things, 2020, 4(4): 26-31. DOI： 10.11959/j.issn.2096-3750.2020.00202.

摘要

为了解决井下粉尘环境和遮挡条件下的人员行为识别问题，促进煤矿安全生产，提出了一种基于 Wi-Fi信道状态信息（CSI

channel state information）的井下人员行为识别方法。该方法采用Hampel滤波结合中位数滤波处理CSI原始数据，并通过线性校正方法利用相位信息。行为识别过程分为离线和在线两个阶段，离线阶段通过采集人员的不同活动信息来建立判识模型，在线阶段根据判识模型识别当前动作。在实验中设置了8个不同的人员活动，实验结果表明，该系统的识别准确率可达95%。

Abstract

To solve the problem of personnel behavior identification under the condition of dust environment and shielding and to promote the coal mine safety production

a personnel identification method based on the Wi-Fi channel state information (CSI) was proposed.The system used Hampel filter and median filter to process the raw CSI data

and utilized the phase information through a linear correction method.The recognition process was divided into the offline stage and online stage.In the offline stage

different activities data was collected to establish the recognition model.While in the online stage

current actions were recognized according to the recognition model.8 different human activities were set in the experiments and the result indicated that the recognition accuracy of this system could reach 95%.

关键词

煤矿安全信道状态信息井下人员行为识别Wi-Fi主成分分析

Keywords

coal mine safetychannel state informationunderground personnel behavior identificationWi-Fiprincipal component analysis

references

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