scholarly journals Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3329 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
Sunghwan Kim ◽  
Ahmed A. Ewees ◽  
Aaqif Afzaal Abbasi ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Channel State Information ◽  
Software Radio ◽  
Human Motion ◽  
Indoor Environments ◽  
Channel State ◽  
State Information ◽  
Human Motion Detection ◽  
The Future ◽  
Device Free

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

scholarly journals Multi-Target Intense Human Motion Analysis and Detection Using Channel State Information

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3379 ◽  
Author(s):  
Jialin Liu ◽  
Lei Wang ◽  
Jian Fang ◽  
Linlin Guo ◽  
Bingxian Lu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Motion Detection ◽  
School Bullying ◽  
Human Motion ◽  
Line Of Sight ◽  
Human Motion Analysis ◽  
Channel State ◽  
Detection Scheme ◽  
State Information ◽  
Human Motion Detection

Intense human motion, such as hitting, kicking, and falling, in some particular scenes indicates the occurrence of abnormal events like violence and school bullying. Camera-based human motion detection is an effective way to analyze human behavior and detect intense human motion. However, even if the camera is properly deployed, it will still generate blind spots. Moreover, camera-based methods cannot be used in places such as restrooms and dressing rooms due to privacy issues. In this paper, we propose a multi-target intense human motion detection scheme using commercial Wi-Fi infrastructures. Compared with human daily activities, intense human motion usually has the characteristics of intensity, rapid change, irregularity, large amplitude, and continuity. We studied the changing pattern of Channel State Information (CSI) influenced by intense human motion, and extracted features in the pattern by conducting a large number of experiments. Considering occlusion exists in some complex scenarios, we distinguished the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions in the case of obstacles appearing between the transmitter and the receiver, which further improves the overall performance. We implemented the intense human motion detection system using single commercial Wi-Fi devices, and evaluated it in real indoor environments. The experimental results show that our system can achieve intense human motion detection rate of 90%.

scholarly journals Kullback–Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4783
Author(s):  
Gao ◽  
Zhang ◽  
Xiao ◽  
Li
Keyword(s):  
Channel State Information ◽  
Indoor Localization ◽  
Probabilistic Approach ◽  
Indoor Environments ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Sensing Applications ◽  
Leibler Divergence ◽  
Device Free

Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)—a kind of information collected in the physical layer—is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback–Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach.

scholarly journals A Survey of User Authentication Based on Channel State Information

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhengjie Wang ◽  
Wenwen Dou ◽  
Mingjing Ma ◽  
Xiaoxue Feng ◽  
Zehua Huang ◽  
...  
Keyword(s):  
Channel State Information ◽  
User Authentication ◽  
Human Action ◽  
Fundamental Principle ◽  
Accurate Information ◽  
Channel State ◽  
Research Directions ◽  
State Information ◽  
Device Free ◽  
Open Issues

Recently, human behavior sensing based on WiFi channel state information has drawn more attention in the ubiquitous computing field because it can provide accurate information about the target under a device-free scheme. This paper concentrates on user authentication applications using channel state information. We investigate state-of-the-art studies and survey their characteristics. First, we introduce the concept of channel state information and outline the fundamental principle of user authentication. These systems measure the dynamic channel state information profile and implement user authentication by exploring the channel state information variation caused by users because each user generates unique channel state information fluctuations. Second, we elaborate on signal processing approaches, including signal selection and preprocessing, feature extraction, and classification methods. Third, we thoroughly investigate the latest user authentication applications. Specifically, we analyze these applications from typical human action, including gait, activity, gesture, and stillness. Finally, we provide a comprehensive discussion of user authentication and conclude the paper by presenting some open issues, research directions, and possible solutions.

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