Recent advancements in wireless communication technologies have made Wi-Fi signals indispensable in both personal and professional settings. The utilization of these signals for Human Activity Recognition (HAR) has emerged as a cutting-edge technology. By leveraging the fluctuations in Wi-Fi signals for HAR, this approach offers enhanced privacy compared to traditional visual surveillance methods. The essence of this technique lies in detecting subtle changes when Wi-Fi signals interact with the human body, which are then captured and interpreted by advanced algorithms. This paper initially provides an overview of the key methodologies in HAR and the evolution of non-contact sensing, introducing sensor-based recognition, computer vision, and Wi-Fi signal-based approaches, respectively. It then explores tools for Wi-Fi-based HAR signal collection and lists several high-quality datasets. Subsequently, the paper reviews various sensing tasks enabled by Wi-Fi signal recognition, highlighting the application of deep learning networks in Wi-Fi signal detection. The fourth section presents experimental results that assess the capabilities of different networks. The findings indicate significant variability in the generalization capacities of neural networks and notable differences in test accuracy for various motion analyses.

中文翻译：

---

用于人类活动识别的 Wi-Fi 传感技术：简要调查、潜在挑战和研究方向


无线通信技术的最新进步使 Wi-Fi 信号在个人和专业环境中都不可或缺。将这些信号用于人类活动识别 （HAR） 已成为一项尖端技术。通过利用 HAR 的 Wi-Fi 信号波动，与传统的视觉监控方法相比，这种方法提供了增强的隐私性。这项技术的本质在于检测 Wi-Fi 信号与人体交互时的细微变化，然后通过高级算法捕获和解释这些变化。本文最初概述了 HAR 中的关键方法和非接触式传感的演变，分别介绍了基于传感器的识别、计算机视觉和基于 Wi-Fi 信号的方法。然后，本文探讨了基于 Wi-Fi 的 HAR 信号收集工具，并列出了几个高质量的数据集。随后，本文回顾了 Wi-Fi 信号识别实现的各种传感任务，重点介绍了深度学习网络在 Wi-Fi 信号检测中的应用。第四部分介绍了评估不同网络能力的实验结果。研究结果表明，神经网络的泛化能力存在显著变化，各种运动分析的测试精度存在显著差异。