Abstract

Features extraction and analysis for human activity recognition (HAR) have been studied for decades in the 5th generation (5G) and beyond the 5th generation (B5G) era. Nowadays, with the extensive use of unmanned aerial vehicles (UAVs) in the civil field, integrating wireless signal receivers on UAVs could be a better choice to receive hearable signals more conveniently. In recent years, the HAR system based on CSI based on WiFi radar has received widespread attention due to its low cost and privacy protection property. However, in the existing CSI-based HAR system, there are two disadvantages: (1) The detection threshold is manually set, which limits its adaptability and immediacy in different wireless environments. (2) A sole classifier is used to complete the recognition, resulting in poor robustness and relatively low recognition accuracy. In this paper, we propose a CSI-based device-free HAR (CDHAR) system with WiFi-sensing radar integrated on UAVs to recognize everyday human activities. Firstly, by using machine learning, CDHAR applies kernel density estimation (KDE) to obtain adaptive detection thresholds to complete the extraction of activity duration. Second, we proposed a random subspace classifier ensemble method for classification, which applies the frequency domain feature instead of the time domain feature, and we choose each kind of feature in the same amount. Finally, we prototype CDHAR on commercial WiFi devices and evaluate its performance in both indoor environment and outdoor environments. The experiment results tell that even if experimental scenario varies, the accuracy of activity durations extraction can reach 98% and 99.60% whether in outdoor or indoor environments. According to the extracted data, the recognition accuracy in outdoor and indoor environments can reach 91.2% and 90.2%, respectively. CDHAR ensures high recognition accuracy while improving the adaptability and instantaneity.

中文翻译：

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b5G无线通信中基于信道声明信息的无设备人类活动识别的特征提取和分析

摘要

人类活动识别（HAR）的特征提取和分析在第五代（5G）以及第五代（B5G）时代已经进行了数十年的研究。如今，随着民用无人机的广泛使用，在无人机上集成无线信号接收器可能是更方便地接收可听信号的更好选择。近年来，基于CSI的基于WiFi雷达的HAR系统由于其低成本和隐私保护特性而受到广泛关注。然而，在现有的基于CSI的HAR系统中，存在两个缺点：（1）手动设置检测阈值，这限制了其在不同无线环境中的适应性和即时性。（2）使用唯一的分类器来完成识别，导致鲁棒性差和识别精度相对较低。在本文中，我们提出了一种基于CSI的无设备HAR（CDHAR）系统，该系统将WiFi感应雷达集成到无人机上以识别日常人类活动。首先，通过机器学习，CDHAR应用核密度估计（KDE）来获得自适应检测阈值，以完成对活动持续时间的提取。其次，我们提出了一种随机子空间分类器集成方法进行分类，该方法应用频域特征而不是时域特征，并以相同的数量选择每种特征。最后，我们在商用WiFi设备上制作CDHAR原型，并评估其在室内和室外环境下的性能。实验结果表明，即使实验场景发生变化，活动持续时间提取的准确性也可以达到98％和99。无论是在室外还是室内环境中，都占60％。根据提取的数据，室外和室内环境的识别精度分别可以达到91.2％和90.2％。CDHAR确保高识别精度，同时提高适应性和即时性。