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%。
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%.
作者
张雷
张跃
李明雪
史新国
翟勃
王卫龙
ZHANG Lei;ZHANG Yue;LI Mingxue;SHI Xinguo;ZHAI Bo;WANG Weilong(School of Information Engineering(School of Big Data),Xuzhou University of Technology,Xuzhou 221000,China;IoT(Perception Mine)Research Center,China University of Mining and Technology,Xuzhou 221000,China;School of Electrical and Power Engineering,China University of Mining and Technology,Xuzhou 221000,China;Information Center,Shandong Energy Zibo Mining Group Co.,Ltd.,Zibo 225100,China)
出处
《物联网学报》
2020年第4期26-31,共6页
Chinese Journal on Internet of Things
基金
国家重点研发计划(No.2017YFC0804400)。
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
coal mine safety
channel state information
underground personnel behavior identification
Wi-Fi
principal component analysis
