Wi-Fi CSI-Based Outdoor Human Flow Prediction Using a Support Vector Machine
Wi-Fi CSI-Based Outdoor Human Flow Prediction Using a Support Vector Machine
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DOI：
10.3390/s20072141
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
M. Ogawa;Hirofumi Munetomo
中科院分区：
文献类型：
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作者：
M. Ogawa;Hirofumi Munetomo
提出了一种基于信道状态信息（CSI）的包含活动的人流预测方法。本文的目的是预测人流量在室外道路。这种人流预测对于预测经过的人的数量及其活动是有用的，而没有由于没有任何相机系统而导致的隐私问题。在本文中，我们假设七种类型的活动：一个，两个和三个人步行;一个，两个和三个人跑步;和一个人骑自行车。由于CSI能有效地表达无线信号中多径衰落的影响，我们期望CSI能预测各种活动。在我们所提出的方法中，幅度和相位分量从测量的CSI中提取。用于机器学习的特征值是由流量的幅值或相位分量和通过时间组成的自相关矩阵和方差-协方差矩阵导出的最大特征值的均值和方差。使用这些特征值，我们评估了预测精度的留一交叉验证与线性支持向量机（SVM）。结果表明，该方法在每个方向上的最大预测精度为100%，在两个方向上的最大预测精度为99.5%。
This paper proposes a channel state information (CSI)-based prediction method of a human flow that includes activity. The objective of the paper is to predict a human flow in an outdoor road. This human flow prediction is useful for the prediction of the number of passing people and their activity without privacy issues as a result of the absence of any camera systems. In this paper, we assume seven types of activities: one, two, and three people walking; one, two, and three people running; and one person cycling. Since the CSI can effectively express the effect of multipath fading in wireless signals, we expected the CSI to predict the various activities. In our proposed method, the amplitude and phase components are extracted from the measured CSI. The feature values for machine learning are the mean and variance of the maximum eigenvalue derived from the auto-correlation matrix and variance–covariance matrix composed of the amplitude or phase components and the passing time of flow. Using these feature values, we evaluated the prediction accuracy by leave-one-out cross-validation with a linear support vector machine (SVM). As a result, the proposed method achieved the maximum prediction accuracy of 100% for each direction and 99.5% for two directions.