摘要
提出提出一种基于小波包与随机森林的矿工运动状态识别算法MSR-WPT-RF,用于监测矿工生命体征,识别运动状态.实验测试结果表明,构建的矿工体域网生命体征采集节点具有低功耗性能、丢包率低、时延低等优点,运动状态识别算法能够取得最高91%的识别准确率.
A miner′s motion state recognition algorithm MSR-WPT-RF based on wavelet packet and random forest is proposed to monitor miners′vital signs and identify their motion states.The experimental test and verification results show that the life signs acquisition node of the miners′body area network has low power consumption performance,low packet loss rate and low delay,and the motion state recognition algorithm can achieve the highest recognition accuracy of 91%.
作者
董飞
李彦廷
慕灯聪
赵子含
丰耀辉
葛鲲鹏
DONG Fei;LI Yanting;MU Dengcong;ZHAO Zihan;FENG Yaohui;GE Kunpeng(School of Internet,Anhui University,Hefei 230039,China;School of physics and electronic information,Huaibei Normal University,Huaibei 235000,China;School of Information Engineering,Yangzhou Polytechnic Institute,Yangzhou 225127,China)
出处
《牡丹江师范学院学报（自然科学版）》
2023年第1期12-18,共7页
Journal of Mudanjiang Normal University：Natural Sciences Edition
基金
国家重点研发计划资助项目(2017YFC0804400)
安徽大学大学生创新创业训练计划项目(S202210357246)
淮北师范大学教学研究项目(2021xjxyj037)。
关键词
矿工
生命体征
体域网
随机森林
低功耗
miner
life signs
body area network
random forest
low-power consumption
