As an essential technology in the field of the Internet of Things, Human activity recognition (HAR) is a well-researched topic. Recently, some state-of-the-art WiFi-based HAR systems have been presented due to its characteristics of no-invasion, no privacy leakage, and high recognition accuracy rates (RARs). However, the commodity WiFi devices for identification are usually in a complex electromagnetic environment, where the interference caused by WiFi devices from channel overlap is common and severe. Furthermore, our extensive experiments show that the performance of these pioneer WiFi-based HAR systems may degrade significantly in co-channel interference (CCI) scenarios. To solve the above problem, we propose WiAnti, a WiFi-based HAR system that is robust to CCI. Two adaptive subcarrier selection algorithms, WiAnti-Pearson and WiAnti-DTW, are proposed to mitigate the impact of CCI and to improve the recognition performance in CCI scenarios. As demonstrated in the experimental results, WiAnti-Pearson yields a 95% RAR on average, which can improve up to a 14% RAR in the presence of constant CCI. Moreover, WiAnti-DTW achieves an 8% higher RAR in the varying CCI scenario, reaching 94%.

中文翻译：

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基于WiFi子载波相关性选择的抗干扰人体活动识别


作为物联网领域的一项关键技术，人类活动识别（HAR）是一个深入研究的课题。最近，一些最先进的基于WiFi的HAR系统因其无入侵、无隐私泄露和高识别准确率（RAR）的特点而被提出。然而，用于识别的商用WiFi设备通常处于复杂的电磁环境中，WiFi设备造成的信道重叠干扰普遍且严重。此外，我们的大量实验表明，这些领先的基于 WiFi 的 HAR 系统的性能在同信道干扰 (CCI) 场景中可能会显着下降。为了解决上述问题，我们提出了 WiAnti，一种基于 WiFi 的 HAR 系统，对 CCI 具有鲁棒性。提出了两种自适应子载波选择算法WiAnti-Pearson和WiAnti-DTW来减轻CCI的影响并提高CCI场景中的识别性能。实验结果表明，WiAnti-Pearson 的平均 RAR 为 95%，在 CCI 恒定的情况下，RAR 最多可提高 14%。此外，WiAnti-DTW 在不同 CCI 场景下的 RAR 提高了 8%，达到 94%。