Human Behavior Recognition Using Wi-Fi CSI: Challenges and Opportunities

Chen, Lili; Liu, Chen; Ligang, Ni; Peng, Yao; Fang, Dingyi

doi:10.1109/mcom.2017.1700081

Cited by 31 publications

(14 citation statements)

References 7 publications

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“…In other words, when a target goes across different Fresnel areas, the signal path formed by the body reflection will vary with the different Fresnel area. Specifically, the different behaviors can be recognized by analyzing the signal path changes, such as walking direction estimation [129], respiratory detection [131]- [135], [193], human detection [126], and behavior recognition [125], [194].…”

Section: ) Fresnel Zone Modelmentioning

confidence: 99%

A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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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 patternbased, 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. INDEX TERMS Channel state information (CSI), deep learning, human behavior recognition, model, pattern, WiFi.

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Section: ) Fresnel Zone Modelmentioning

confidence: 99%

A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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“…Finally, the DOA estimation is obtained by improving the time difference estimation accuracy in terms of arrival. In [22], another three-step joint estimation algorithm was proposed. First, the maximum likelihood estimation algorithm is used to perform the preliminary TOA estimation, and then a further joint estimation of the TOA and the arrival time difference is performed.…”

Section: Related Workmentioning

confidence: 99%

“…In the second step, formulas (22) to (24) are used to determine the intermediate variables. Finally, formula (20) is used to determine the real-time coordinates X, Y, and Z.…”

Section: Spatial Positioning Modelmentioning

confidence: 99%

A Method for Improving UWB Indoor Positioning

Hao

Dang

2018

Mathematical Problems in Engineering

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The existing positioning methods that use received signal strength indication (RSSI) and channel state information (CSI) may suffer from multipath and shadowing in a complex wireless environment, which can result in more positioning errors. This paper proposes a method for accurate multilabel positioning in the non-line-of-sight (NLOS) environment. First, the position is roughly estimated using the orthogonal variable spreading factor (OVSF-TH) algorithm, which can automatically match the signal interference. The ultra-wideband (UWB) spectral density and pulse amplitude in the time domain are used to determine the direction of the label and enhance estimation of the mobile label direction. Then, the location of the tag is obtained by triangulation, and a coordinate-based coordinate estimation method is proposed to calculate the relative displacement of multiple tags to determine the label position. Finally, by setting up a real experimental environment, the influence of the number of base stations on the accuracy and the performance of the localization method under different circumstances are analyzed. The theoretical analysis and experimental results show that the method is simple to deploy, inexpensive, and very accurate in terms of positioning, having a clearly effective indoor positioning accuracy. Compared with other existing positioning methods, this method can achieve more accurate positioning. Moreover, it has important theoretical and practical applicability because of the reliability and accuracy of indoor positioning in an NLOS environment.

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“…According to the different recognition algorithms and application scenes, a WiFi-based human motion recognition system can be divided into two categories: one is the modelbased recognition system and the other is the fingerprint recognition system. e main difference between them is whether a priori learning is required [8]. e model-based recognition system can recognize human motion without training.…”

Section: Introductionmentioning

confidence: 99%

CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

Hao

Duan

Dang

et al. 2020

Mobile Information Systems

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WiFi indoor personnel behavior recognition has become the core technology of wireless network perception. However, the existing human behavior recognition methods have great challenges in terms of detection accuracy, intrusion, and complexity of operations. In this paper, we firstly analyze and summarize the existing human motion recognition schemes, and due to the existence of the problems in them, we propose a noninvasive, highly robust complex human motion recognition scheme based on Channel State Information (CSI), that is, CSI-HC, and the traditional Chinese martial art XingYiQuan is verified as a complex motion background. CSI-HC is divided into two phases: offline and online. In the offline phase, the human motion data are collected on the commercial Atheros NIC and a powerful denoising method is constructed by using the Butterworth low-pass filter and wavelet function to filter the outliers in the motion data. Then, through Restricted Boltzmann Machine (RBM) training and classification, we establish offline fingerprint information. In the online phase, SoftMax regression is used to correct the RBM classification to process the motion data collected in real time and the processed real-time data are matched with the offline fingerprint information. On this basis, the recognition of a complex human motion is realized. Finally, through repeated experiments in three classical indoor scenes, the parameter setting and user diversity affecting the accuracy of motion recognition are analyzed and the robustness of CSI-HC is detected. In addition, the performance of the proposed method is compared with that of the existing motion recognition methods. The experimental results show that the average motion recognition rate of CSI-HC in three classic indoor scenes reaches 85.4%, in terms of motion complexity and indoor recognition accuracy. Compared with other algorithms, it has higher stability and robustness.

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Human Behavior Recognition Using Wi-Fi CSI: Challenges and Opportunities

Cited by 31 publications

References 7 publications

A Survey on Human Behavior Recognition Using Channel State Information

A Survey on Human Behavior Recognition Using Channel State Information

A Method for Improving UWB Indoor Positioning

CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

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