Accuracy evaluations of human moving pattern using communication quality based on machine learning

机译：基于机器学习的通信质量对人类运动模式的准确性评估

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In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude that the communication quality can recognize moving patterns with high accuracy.

机译：在本文中，我们使用通信质量执行了人类运动模式识别：蜂窝下载吞吐量，接收信号强度指示器（RSSI）和蜂窝基站ID。我们应用了三种机器学习算法，例如支持向量机（SVM），K最近邻（KNN）和随机森林（RF），并评估了人类运动模式的识别准确性。结果表明，通信质量可以高精度地识别运动模式。

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《IEEE Global Conference on Consumer Electronics》|2017年|1-2|共2页
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Wataru Kawakami; Kenji Kanai; Bo Wei; Jiro Katto;
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Pattern recognition; Conferences; Support vector machines; Throughput; Base stations; Public transportation; Mobile communication;

机译：模式识别;会议;支持向量机;吞吐量;基站;公共交通;移动通信;

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Accuracy evaluations of human moving pattern using communication quality based on machine learning

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