scholarly journals R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1421 ◽  
Author(s):  
Jijun Zhao ◽  
Lishuang Liu ◽  
Zhongcheng Wei ◽  
Chunhua Zhang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Motion Detection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Research Area ◽  
Human Motion ◽  
Average Error ◽  
Area Of Interest ◽  
Human Motion Detection ◽  
Average Error Rate ◽  
Duration Estimation

As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively.

scholarly journals Theoretical Investigations for the Verification of Shear Centre and Deflection of Sigma Section by Back Propagation Neural Network Using Python

2019 ◽  
Vol 65 (2) ◽  
pp. 181-192
Author(s):  
S. Janani ◽  
R. Thenmozhi ◽  
L.S. Jayagopal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Error Rate ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Average Error ◽  
Average Error Rate ◽  
Proposed Model ◽  
Theoretical Investigations ◽  
Shear Centre

AbstractThe most important challenges in the construction field is to do the experimentation of the designing at real time. It leads to the wastage of the materials and time consuming process. In this paper, an artificial neural network based model for the verification of sigma section characteristics like shear centre and deflection are designed and verified. The physical properties like weight, depth, flange, lip, outer web, thickness, and area to bring shear centre are used in the model. Similarly, weight, purlin centres with allowable loading of different values used in the model for deflection verification. The overall average error rate as 1.278 percent to the shear centre and 2.967 percent to the deflection are achieved by the model successfully. The proposed model will act as supportive tool to the steel roof constructors, engineers, and designers who are involved in construction as well as in the section fabricators industry.

scholarly journals Flexible and wearable strain sensor based on electrospun carbon sponge/polydimethylsiloxane composite for human motion detection

RSC Advances ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 4186-4193
Author(s):  
He Gong ◽  
Chuan Cai ◽  
Hongjun Gu ◽  
Qiushi Jiang ◽  
Daming Zhang ◽  
...  
Keyword(s):  
Motion Detection ◽  
Strain Sensor ◽  
Human Motion ◽  
Gauge Factor ◽  
Human Motion Detection ◽  
Tensile Strains

Electrospun carbon sponge was used to measure tensile strains with a high gauge factor.

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%.

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