Faculty of Science and Technology, Sophia University, Tokyo 102-8554, Japan.
Sensors (Basel). 2020 Apr 10;20(7):2141. doi: 10.3390/s20072141.
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.
本文提出了一种基于信道状态信息（CSI）的人类流动活动预测方法。本文的目的是预测户外道路上的人流。这种人流预测对于预测通过的人数及其活动是有用的，因为没有任何摄像系统，不会存在隐私问题。在本文中，我们假设了七种活动类型：一人、二人、三人步行；一人、二人、三人跑步；以及一人骑自行车。由于 CSI 可以有效地表达无线信号中多径衰落的影响，我们期望 CSI 可以预测各种活动。在我们提出的方法中，从测量的 CSI 中提取幅度和相位分量。机器学习的特征值是自相关矩阵和方差-协方差矩阵的最大特征值的均值和方差，该矩阵由幅度或相位分量和流动通过时间组成。使用这些特征值，我们使用线性支持向量机（SVM）进行了留一法交叉验证，以评估预测精度。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%。
