Recently, with the advancement of the Internet of Things (IoT), WiFi CSI-based HAR has gained increasing attention from academic and industry communities. By integrating the deep learning technology with CSI-based HAR, researchers achieve state-of-the-art performance without the need of expert knowledge. However, the scarcity of labeled CSI data remains the most prominent challenge when applying deep learning models in the context of CSI-based HAR due to the privacy and incomprehensibility of CSI-based HAR data. On the other hand, SSL has emerged as a promising approach for learning meaningful representations from data without heavy reliance on labeled examples. Therefore, considerable efforts have been made to address the challenge of insufficient data in deep learning by leveraging SSL algorithms. In this paper, we undertake a comprehensive inventory and analysis of the potential held by different categories of SSL algorithms, including those that have been previously studied and those that have not yet been explored, within the field. We provide an in-depth investigation of SSL algorithms in the context of WiFi CSI-based HAR. We evaluate four categories of SSL algorithms using three publicly available CSI HAR datasets, each encompassing different tasks and environmental settings. To ensure relevance to real-world applications, we design performance metrics that align with specific requirements. Furthermore, our experimental findings uncover several limitations and blind spots in existing work, highlighting the barriers that need to be addressed before SSL can be effectively deployed in real-world WiFi-based HAR applications. Our results also serve as a practical guideline for industry practitioners and provide valuable insights for future research endeavors in this field.
SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing
Nanyang Technological University（南洋理工大学）；University of Edinburgh（爱丁堡大学）；Institute for Infocomm Research（信息通信研究院）；Agency for Science, Technology and Research（新加坡科技研究局）
Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data
School of Engineering, Institute of Science Tokyo（东京科学研究院工程学院）；Wireless Systems R&D Department, Infrastructure Systems R&D Center, Corporate Laboratory, Toshiba Corporation（东芝公司无线系统研发部、基础设施系统研发中心、企业实验室）
WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition
Department of Computing, School of Electrical Engineering and Computer Science（计算机系，电气工程与计算机科学学院）；National University of Sciences and Technology (NUST)（国家科学与技术大学（NUST））
Meta-Learning-Based People Counting and Localization Models Employing CSI from Commodity WiFi NICs
School of Electrical Engineering, Korea Advanced Institute of Science and Technology（韩国科学技术院电气工程学院）；School of Electronics Engineering, Kyungpook National University（庆北国立大学电子工程学院）
High-Fidelity and Location-Robust Respiratory Waveform Monitoring with Single-Antenna WiFi
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Zhejiang Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing（浙江省多模通信网络与智能信息处理重点实验室）；School of Information and Electrical Engineering, Hangzhou City University（杭州城市大学信息与电气工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
WiFi Motion Detection: A Study into Efficacy and Classification
WiFi和安全性既是一个问题，也是日常生活中越来越多的存在。今天的运动检测实现在保密性、范围和成本方面严重不足。为了解决这个问题，我们的目标是开发一种运动检测系统，利用WiFi信道状态信息（CSI），它描述了无线信号如何从发射机传播到接收机。本研究的目标是开发一个实时的运动检测和分类系统，是谨慎的，具有成本效益，易于实现。该系统只需要一台配备Intel Ultimate N WiFi Link 5300和标准路由器的Ubuntu笔记本电脑。该系统将分为两个部分：（1）一个强大的系统，以跟踪CSI的变化，在实时，和（2）算法，以分类的运动。用于实时跟踪CSI差异的系统已于2018年8月完成。初步结果表明，引入运动到一个先前静止的区域检测具有高的置信度。我们提出了（1）异常检测的发展，利用在初始程序和/或无监督机器学习中实现的移动平均滤波器，以及（2）监督机器学习算法，使用提出的特征提取方法对一组简单运动进行分类。最后，分类方法，如决策树，朴素贝叶斯和长短期记忆，可用于分类基本动作，无论速度，位置或方向。
Beamforming Feedback-based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study
Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；School of Engineering, Tokyo Institute of Technology（东京工业大学工学院）；Centre of Wireless Communications, University of Oulu（奥卢大学无线通信中心）
Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Florida State University（佛罗里达州立大学）；Bosch Research and Technology Center（博世研究技术中心）
基于信道状态信息（Channel State Information，CSI）的指纹识别技术在WIFI室内定位中得到了广泛的关注，频率多样且时间稳定的CSI比粗接收信号强度（Recovered Signal Strength，RSS）更能反映位置相关的信道特性。然而，CSI的获取需要接入点（AP）的合作并且仅涉及多个帧，这对现实世界的部署施加了限制。在本文中，我们提出了CRISLoc，第一个CSI指纹定位原型系统使用无处不在的智能手机。CRISLoc在一个完全被动的模式下运行，在他自己的CSI采集过程中偷听数据包。通过校准WiFi放大器电路施加的失真来净化智能手机CSI。CRISLoc通过联合聚类和离群值检测方法来解决改变AP的挑战。提出了一种新的迁移学习方法，在过时的指纹和一些新的测量的基础上重建高维CSI指纹数据库，并提出了一种增强的KNN方法来精确定位智能手机的位置。我们的研究揭示了智能手机CSI的稳定性和灵敏度的重要属性，这是以前没有报道过的。实验结果表明，CRISLoc在6m × 8m的研究实验室中可以实现约0.29m的平均误差。移动一个和两个AP后，平均误差分别增加了5.4 cm和8.6 cm，这验证了CRISLoc对环境变化的鲁棒性。
