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 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 Indoor Motion Detection Using Wi-Fi Channel State Information in Flat Floor Environments Versus in Staircase Environments

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2177 ◽  
Author(s):  
Zehua Dong ◽  
Fangmin Li ◽  
Julang Ying ◽  
Kaveh Pahlavan
Keyword(s):  
Channel State Information ◽  
Motion Detection ◽  
Channel State ◽  
State Information

scholarly journals SVD-Aided Power Allocation and Iterative Detection Scheme for Turbo-BLAST System with Imperfect Channel State Information

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaomin Chen ◽  
Xiangbin Yu ◽  
Dazhuan Xu
Keyword(s):  
Power Allocation ◽  
Channel State Information ◽  
Mimo Channel ◽  
Iterative Detection ◽  
Imperfect Channel State Information ◽  
Channel State ◽  
Detection Scheme ◽  
State Information ◽  
Adaptive Power ◽  
Ber Performance

A new technique that combines adaptive power allocation and iterative detection based on singular value decomposition (SVD) is introduced for the modified Turbo-BLAST system with imperfect channel state information (I-CSI). At the transmitter, in order to maximize the capacity performance, the MIMO channel is decomposed into several parallel eigen subchannels by SVD, and then proper power based on the water-filling principle is allocated to every subchannel subject to the total transmit power constraint. At the receiver, the modified MMSE detector taking the CSI imperfection into account is used to remove the coantenna interference, and then the turbo idea is employed for iterative detection to lower the system BER. As a result, the BER performance is effectively enhanced. Numerical results show that the introduced SVD-aided adaptive power allocation method is valid to improve not only the capacity but also the BER performance in the presence of channel state information imperfection, while the iterative detector can further lower the BER results.

scholarly journals NLOS Identification in WLANs Using Deep LSTM with CNN Features

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4057 ◽  
Author(s):  
Viet-Hung Nguyen ◽  
Minh-Tuan Nguyen ◽  
Jeongsik Choi ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Channel State Information ◽  
Recurrent Neural Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Channel State ◽  
State Information ◽  
Channel Conditions

Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for orthogonal frequency division multiplexing systems in indoor wireless local area networks. This paper proposes a joint convolutional neural network and recurrent neural network architecture to classify channel conditions. Convolutional neural networks extract the feature from frequency-domain characteristics of channel state information data and recurrent neural networks extract the feature from time-varying characteristics of received signal strength identification and channel state information between packet transmissions. The performance of the proposed methods is verified under indoor propagation environments. Experimental results show that the proposed method has a 2% improvement in classification performance over the conventional recurrent neural network model.

Iterative Detection Scheme for Turbo-BLAST System with Adaptive Power Allocation in the Presence of Channel State Information Imperfection

2013 ◽  
Vol 646 ◽  
pp. 120-125
Author(s):  
Xiao Min Chen ◽  
Xiao Dan Yu ◽  
Wei Tan ◽  
Xiang Bin Yu
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Channel State Information ◽  
Iterative Detection ◽  
Channel State ◽  
Estimation Errors ◽  
Detection Scheme ◽  
State Information ◽  
Channel Estimation Errors ◽  
Optimal Power

We propose an iterative detection scheme for Turbo-BLAST system with optimal power allocation in the presence of channel state information imperfection. The proposed scheme uses the channel estimation matrix for detection and treats the interference caused by channel estimation errors and additive white Gaussian noise as equivalent noise where the channel estimation matrix and the statistical characteristic of channel estimation errors are necessitated. Simulation results show the proposed algorithm is effective to improve bit error rate (BER) performance through iterative detection for modified Turbo-BLAST system with optimal power allocation in the presence of imperfect channel state information.

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