Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study
Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study
复制标题
DOI：
10.1109/tim.2013.2258772
复制
发表时间：
2013-09-01
影响因子：
5.6
通讯作者：
Marsic, Ivan
中科院分区：
文献类型：
--
作者：
Parlak, Siddika;Marsic, Ivan
研究了利用射频识别技术在室内环境中进行目标运动检测。与以前的工作不同，我们专注于动态场景，例如受人和许多RFID标签信号干扰的紧急医疗情况。我们建立了一个现实的创伤复苏设置，并记录了大约14,000个检测实例的数据集。我们发现，影响无线电信号的因素，如标签运动，具有不同的统计指纹，使得它们可以使用统计方法进行识别。我们的目标运动检测方法提取接收信号强度的描述性特征，并使用机器学习技术对其进行分类。我们报告了几种统计特征和分类器的实验结果，并为不同环境下的特征和分类器的选择提供了指导。实验结果表明，在复杂场景下，目标运动检测的准确率为80%，平均准确率为90%。另一方面，使用当前可用的无源RFID技术不能以如此高的精度识别运动类型。
We studied object motion detection in an indoor environment using RFID technology. Unlike prior work, we focus on dynamic scenarios, such as emergency medical situations, subject to signal interference by people and many RFID tags. We build a realistic trauma resuscitation setting and record a dataset of around 14 000 detection instances. We find that factors affecting radio signal, such as tag motion, have different statistical fingerprints, making them discernible using statistical methods. Our method for object motion detection extracts descriptive features of the received signal strength and classifies them using machine-learning techniques. We report experimental results obtained with several statistical features and classifiers, and provide guidelines for feature and classifier selection in different environments. Experimental results show that object motion could be detected with an accuracy of 80% in complex scenarios and 90% on average. The motion type, on the other hand, could not be identified with such high accuracy using currently available passive RFID technology.