Wiar: A Public Dataset for Wifi-Based Activity Recognition
Wiar: A Public Dataset for Wifi-Based Activity Recognition
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DOI：
10.1109/access.2019.2947024
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Guo, Silu
中科院分区：
文献类型：
--
作者：
Guo, Linlin;Wang, Lei;Guo, Silu
我们构建了一个公共数据集的WiFi为基础的活动识别命名为WiAR 16个活动，由10名志愿者在三个室内环境。它旨在为研究人员提供公共信号数据，以降低采集信号数据的成本，并方便地评估基于WiFi的人体活动识别在不同领域的性能。首先，我们介绍WiFi信号的RSSI，CSI和无线硬件的基本知识。其次，我们解释了WiAR数据集的活动类型，数据格式，数据采集方式和影响因素的特点。第三，所提出的框架可以估计由其他对等体提供的共享信号数据的质量。最后，我们选择并使用五种分类算法和两种深度学习算法来评估WiAR数据集在人体活动识别上的性能。结果表明，在不同的室内环境中，使用机器学习算法的WiAR数据集的准确率高于80%，使用深度学习算法的准确率高于90%。
We construct a public dataset for WiFi-based Activity Recognition named WiAR with sixteen activities operated by ten volunteers in three indoor environments. It aims to provide public signal data for researchers to reduce the cost of collected signal data and conveniently evaluate the performance of WiFi-based human activity recognition in different domains. First, we introduce the basic knowledge of WiFi signals regarding RSSI, CSI, and wireless hardware. Second, we explain the characteristics of WiAR dataset in terms of activities types, data format, data acquisition ways, and influence factors. Third, the proposed framework can estimate the quality of the shared signal data provided by other peers. Finally, we select and use five classification algorithms and two deep learning algorithms to evaluate the performance of WiAR dataset on human activity recognition. The results show that the accuracy of WiAR dataset is higher than 80% using machine learning algorithms and 90% using deep learning algorithms in different indoor environments.