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。
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.
引文
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