计算机工程 ›› 2019, Vol. 45 ›› Issue (6): 297-302,309.doi: 10.19678/j.issn.1000-3428.0050714

• 开发研究与工程应用 • 上一篇    下一篇

基于CRFID和模式识别的跌倒检测系统

闫玉娟,李化,赵菊敏,李灯熬,刘佳   

  1. 太原理工大学 信息与计算机学院,山西 晋中 030600
  • 收稿日期:2018-03-12 出版日期:2019-06-15 发布日期:2019-06-15
  • 作者简介:闫玉娟(1990—),女,硕士研究生,主研方向为RFID技术;李化,讲师、博士;赵菊敏(通信作者),教授、博士;李灯熬,教授、博士、博士生导师;刘佳,博士研究生。
  • 基金项目:
    国家高技术研究发展计划(2015AA016901);国家自然科学基金(61572346,61772358,61572347);山西省国际科技合作项目(201603D421012);2016年青年骨干教师出国研修项目(201606935043)。

Fall detection system based on CRFID and pattern recognition

YAN Yujuan,LI Hua,ZHAO Jumin,LI Deng’ao,LIU Jia   

  1. College of Information and Computer,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
  • Received:2018-03-12 Online:2019-06-15 Published:2019-06-15

摘要: 根据老年人跌倒发生率高、损伤大、抢救率低等特点,结合可计算射频识别标签(CRFID)与模式识别技术,设计一个改进的跌倒检测系统。通过CRFID采集状态信号,由经验模态分解获取信号的时频特征,对该特征进行主成分分析降维,并使用随机森林算法检测跌倒状况。实验结果表明,该系统的查全率、精确度、转移性和准确率分别为97.75%、97.9%、98%、97.8%,能实时、准确地检测老年人的跌倒行为。

关键词: 可计算射频识别标签, 模式识别, 主成分分析, 随机森林, 跌倒检测

Abstract: According to the characteristics of the fall among elder,such as high incidence,great injury,and low rescue rate,combining Computer Radio Frequency Identification (CRFID) and pattern recognition technology,this paper designs an improved fall detection system.Firstly,the system collects the state signals by CRFID.Secondly,the state signals are decomposed by Empirical Mode Decomposition(EMD) to obtain the time-frequency features.Thirdly,Principal Component Analysis(PCA) is used to reduce the dimension of the features.Finally,the Random Forest(RF) is used to detect the falling condition.Experimental results show that the recall,precision,transferability and accuracy of the system are 97.75%,97.9%,98%,and 97.8%,respectively.The system can detect the fall behavior of the elderly accurately in real time.

Key words: Computational Radio Frequency Identification(CRFID), pattern recognition, Principal Component Analysis(PCA), Random Forest (RF), fall detection

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