With the advancement of wireless technologies and sensing methodologies, many studies have shown that wireless signals can sense human behaviors. Human activity recognition using channel state information (CSI) in commercial WiFi devices plays an important role in many applications. In this paper, a framework for human activity recognition was constructed based on WiFi CSI signal enhancement. Firstly, the sensitivity of different antennas to human activity was studied. An antenna selection algorithm was proposed, which can make a choice of the antenna automatically based on their sensitivity in accordance with different activities. Secondly, two signal enhancement approaches, which can strengthen the active signals and weaken the inactive signals, were proposed to extract the active interval caused by human activity. Finally, an activity segmentation algorithm was proposed to detect the start and end time of activity. In order to verify and evaluate the methods, extensive experiments have been conducted in real indoor environments. The experimental results have demonstrated that our solutions can eliminate a large number of redundant information brought by insensitive and inactive signals. Our research results can be put into use to improve recognition accuracy significantly and decrease the cost of recognition time.

中文翻译：

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基于WiFi CSI信号增强的人类活动识别框架

随着无线技术和传感方法的发展，许多研究表明，无线信号可以感知人类的行为。在商用WiFi设备中使用通道状态信息（CSI）进行人类活动识别在许多应用中起着重要作用。本文基于WiFi CSI信号增强技术构建了人类活动识别框架。首先，研究了不同天线对人类活动的敏感性。提出了一种天线选择算法，该算法可以根据天线的灵敏度根据不同的活动自动选择天线。其次，提出了两种信号增强方法，可以增强活动信号并减弱非活动信号，以提取人为活动引起的活动间隔。最后，提出了一种活动分割算法来检测活动的开始和结束时间。为了验证和评估这些方法，已经在真实的室内环境中进行了广泛的实验。实验结果表明，我们的解决方案可以消除不敏感和无效信号带来的大量冗余信息。我们的研究结果可用于显着提高识别精度并减少识别时间的成本。