A state classification method based on space-time signal processing using SVM for wireless monitoring systems
A state classification method based on space-time signal processing using SVM for wireless monitoring systems
复制标题
基于SVM时空信号处理的无线监测系统状态分类方法
DOI：
10.1109/pimrc.2011.6139913
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
T. Ohtsuki
中科院分区：
文献类型：
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作者：
Jihoon Hong;T. Ohtsuki
在本文中，我们专注于改进状态分类方法，可以在老年人护理监控系统中实施。作者小组先前提出了一种室内监控和安全系统（阵列传感器），该系统仅使用一个阵列天线作为接收器。与传统系统相比，明显的优点是改善了使用闭路电视（CCTV）摄像机带来的隐私问题，并消除了安装困难。我们的方法不同于以前的检测方法，它使用一系列传感器和一个阈值，只能分类两种状态：什么都没有和发生的事情。在本文中，我们提出了一种状态分类方法，只使用一个功能，从无线电波传播，并辅助多类支持向量机（SVM）分类发生的状态。该特征是跨越感兴趣的信号子空间的第一特征向量。该方法不仅适用于室内环境，也适用于室外环境，如车辆监控系统。我们进行了实验，将室内环境中的七种状态分类为：“无事件”，“行走”，“进入浴缸”，“淋浴时站立”，“淋浴时坐着”，“摔倒”和“昏倒”;以及室外环境中的两种状态：“正常状态”和“异常状态”。实验结果表明，我们可以达到96.5%和100%的分类准确率为室内和室外设置，分别。
In this paper we focus on improving state classification methods that can be implemented in elderly care monitoring systems. The authors group has previously proposed an indoor monitoring and security system (array sensor) that uses only one array antenna as the receiver. The clear advantages over conventional systems are improvement of privacy concern from the usage of closed-circuit television (CCTV) cameras, and elimination of installation difficulties. Our approach is different from the previous detection method which uses an array of sensors and a threshold that can classify only two states: nothing and something happening. In this paper, we present a state classification method that uses only one feature obtained from the radio wave propagation, and assisted by multiclass support vector machines (SVM) to classify the occurring states. The feature is the first eigenvector that spans the signal subspace of interest. The proposed method can be applied to not only indoor environments but also outdoor environments such as vehicle monitoring system. We performed experiments to classify seven states in an indoor setting: “No event,” “Walking,” “Entering into a bathtub,” “Standing while showering,” “Sitting while showering,” “Falling down,” and “Passing out;” and two states in an outdoor setting: “Normal state” and “Abnormal state.” The experimental results show that we can achieve 96.5 % and 100 % classification accuracy for indoor and outdoor settings, respectively.