Part I The Background

1 Human Sensing Modalities and Applications

1.1 What is Wireless Sensing

1.1.1 Definition

1.1.2 Wireless Signals

1.2 Characteristics of Wireless Sensing

1.3 Applications of Wireless Sensing

1.3.1 Smart Home

1.3.2 Security Surveillance

Part II Getting Started

2 Main Steps for Wireless Sensing

2.1 Data Collection

2.2 Signal Preprocessing

2.3 Feature Extraction

2.4 Model Training and Inference

3 The Background of Passive Human Detection

3.1 Motivation

3.2 Related Work

4.1 Introduction

4.2 System Overview

4.3 Methodology

4.3.1 Data Processing

4.3.2 Feature Extraction

4.3.3 Motion Detection

4.3.4 Enhancement via Multiple Antennas

4.4 Experiments and Results

4.4.2 Performance Evaluation

4.5 Conclusions

5 Detection of Moving and Stationary Human with Wi-Fi

5.2 Preliminary

5.3 System Design

5.3.1 Overview

5.3.2 Motion Inference Indicator

5.3.3 Moving Target Detection

5.4 Stationary Target Detection

5.4.1 Periodic Alterations from Breathing

5.4.2 Breathing Detection

5.5 Experiments and Evaluation

5.5.1 Implementation

5.5.2 Performance

5.6 Discussions and Future Works

5.6.1 Monitoring Breathing Rate

5.6.2 Expanding Detection Coverage via Space Diversity

5.6.3 Multiple Target Detection

5.6.4 Extending to Through-Wall Detection

5.7 Conclusions

6 Omnidirectional Human Detection with Wi-Fi

6.1 Introduction

6.2 Preliminaries

6.2.2 Signal Power Features

6.3 Feature Extraction and Classification

6.3.1 Sensitivity to Human Presence

6.3.3 Modeling CFR Amplitude Features

6.3.4 Signature Classification

6.4 Human Detection

6.5.1 Experiment Methodology

6.5.2 Static Detection Performance

6.5.3 The Impact of Window Size

6.5.4 Mobile Detection Performance

6.6 Conclusion

Part IV Localization: Passive Human Localization with Wireless Signals

7 The Background of Passive Human Localization

7.1 Motivation

7.2 Related Work

8 Human Localization via Velocity Monitoring with Wi-Fi

8.1 Introduction

8.2.1 Channel State Information

8.2.2 From CSI to PLCR

8.2.3 Challenges for Tracking

8.3 Modeling of CSI-Mobility

8.3.1 The Ideal Model

8.3.2 The Real Model

8.4 PLCR Extraction

8.4.1 CSI Preprocessing

8.4.3 PLCR Sign Identification

8.5 Tracking Velocity & Location

8.5.1 Movement Detection

8.5.3 Successive Tracking

8.5.4 Trace Refinement

8.6 Evaluation

8.6.1 Experiment Methodology

8.6.2 Overall Performance

8.6.3 Parameter Study

8.7 Conclusion

9 Human Localization with a Single Wi-Fi Link

9.2 Overview

9.3 Motion in CSI

9.3.1 CSI-Motion Model

9.3.2 Joint Multiple Parameter Estimation

9.3.3 CSI Cleaning

9.4 Localization

9.4.1 Path Matching

9.4.2 Range Refinement

9.5 Evaluation

9.5.1 Experiment M
About the Author:

Zheng Yang is an associate professor at the School of Software and BNRist, Tsinghua University. He holds a BE degree from Tsinghua University, and a PhD degree from Hong Kong University of Science and Technology. His research interests include Internet of Things, mobile computing, pervasive computing, industrial internet, smart city, etc. He is the author and co-author of 3 books and over 100 papers published in leading journals and conferences. Zheng received the China National Natural Science Award (2011). He is a senior member of IEEE and a member of ACM.

Kun Qian is a post-doctoral researcher in the Department of Electrical and Computer Engineering, University of California San Diego. He received his Ph.D in 2019 at the School of Software, Tsinghua University. He received his B.E. in 2014 in Software Engineering from School of Software, Tsinghua University. His research interests include mobile computing and wireless sensing, etc. He has published over 20 papers in competitive conferences and journals.

Chenshu Wu is an assistant professor at the University of Hong Kong. He is also the Chief Scientist at Origin Wireless Inc. His research focuses on wireless AIoT systems at the intersection of wireless sensing, ubiquitous computing, and the Internet of Things. He has published two books, over 60 papers in prestigious conferences and journals, and over 40 patents. His research has been commercialized as products, including LinkSys Aware that won the CES 2020 Innovation Award, HEX Home that won CES 2021 Innovation Award, and Origin Health Remote Patient Monitoring that won CES 2021 Best of Innovation Award. He holds BS and PhD degrees in Computer Science both from Tsinghua University.

Yi Zhang is currently working toward his PhD degree at the School of Software in Tsinghua University. Prior to that, he received his BE degree from the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, in 2017. His research interests include wireless sensing, mobile computing, and machine learning. He is the author and co-author of over 6 papers published in leading journals and conferences.