A Survey on Human Behavior Recognition Using Channel State Information

Wenwen Dou,Zhengjie Wang,Kangkang Jiang,Yushan Hou,Zehua Huang,Yinjing Guo,Chengming Zhang

doi:10.1109/access.2019.2949123

Wenwen Dou, Zhengjie Wang + Show 5 more

Open Access

https://doi.org/10.1109/access.2019.2949123

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Journal: IEEE Access	Publication Date: Jan 1, 2019
Citations: 81	License type: CC BY 4.0

Affiliation: Shandong University of Science and Technology

Abstract

Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.

Highlights

Recent years have witnessed the rapid development of the Internet of Things (IoT)
We collect Channel state information (CSI) data using a PC equipped with a network interface card (NIC) when the PC communicates with the access point (AP)
We find that most applications apply common deep models (e.g., Autoencoder, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Residual Neural Network (ResNet)), which indicates that the general deep learning algorithm can be used at CSI-based behavior recognition and the

Summary

Introduction

Recent years have witnessed the rapid development of the Internet of Things (IoT). The demand for pervasive computing is dramatically increasing because IoT provides us with useful information about monitoring target and facilities development of ubiquitous computing applications. Ing [4], behavior understanding [5], user profile construction [6], activity inference [7], smart home control [8], human localization [9], human position tracking [10], [11], and occupancy detection [12], etc. These applications of human behavior sensing largely enrich the research content and provide us with novel human-computer interaction methods. With the widespread deployment of WiFi devices in indoor environments, the device-free WiFi-based behavior recognition technique has drawn more attention because it overcomes some existing shortcomings of common behavior recognition

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Discussion

Conclusion