A Survey on Human Behavior Recognition Using Channel State Information

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

doi:10.1109/ACCESS.2019.2949123

IEEE Access (Jan 2019)

A Survey on Human Behavior Recognition Using Channel State Information

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

Affiliations

Zhengjie Wang: ORCiD; College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
Kangkang Jiang: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
Yushan Hou: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
Wenwen Dou: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
Chengming Zhang: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
Zehua Huang: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
Yinjing Guo: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China

DOI: https://doi.org/10.1109/ACCESS.2019.2949123
Journal volume & issue: Vol. 7
pp. 155986 – 156024

Abstract

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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.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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Abstract

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