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Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning Algorithm

Haiqin Zhu

Haiqin Zhu

Yantai Nanshan UniversityYantai, China
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Zhu, Haiqin
, 
Hao Liang

Hao Liang

Yantai Nanshan UniversityYantai, China
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Liang, Hao
, 
Fulai Xiao

Fulai Xiao

Shandong Nanshan Science and Technology Research InstituteYantai, China
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Xiao, Fulai
, 
Gepeng Wang

Gepeng Wang

Nanshan zhonggaoxie International Training CenterYantai, China
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Wang, Gepeng
 y 
Rifat Hussain

Rifat Hussain

College of Administrative Sciences, Applied Science UniversityBahrain
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Hussain, Rifat
  
15 jul 2022
Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning Algorithm's Cover Image
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Publicado en línea: 15 jul 2022
Páginas: 559 - 572
Recibido: 07 feb 2022
Aceptado: 05 abr 2022
DOI: https://doi.org/10.2478/amns.2022.2.0041
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© 2023 Haiqin Zhu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

The Extreme learning model Diagram
The Extreme learning model Diagram

Fig. 2

Schematic diagram of pressure sensor installation
Schematic diagram of pressure sensor installation

Fig. 3

On-Site installation diagram
On-Site installation diagram

Fig. 4

Four lying positions of a person 1
Four lying positions of a person 1

Fig. 5

Four types of backpressure Nephograms for Person 1
Four types of backpressure Nephograms for Person 1

Fig. 6

Geometric feature extraction process
Geometric feature extraction process

Fig. 7

The Pressure image after binarization and canny operation
The Pressure image after binarization and canny operation

Fig. 8

The minimum enclosing moment
The minimum enclosing moment

Fig. 9

Perimeter distribution curve of enclosing rectangle
Perimeter distribution curve of enclosing rectangle

Fig. 10

The area distribution curve of the enclosed rectangle
The area distribution curve of the enclosed rectangle

Fig. 11

Hu Moment
Hu Moment

Fig. 12

Energy characteristic diagram of pressure information
Energy characteristic diagram of pressure information

Fig. 13

ELM results for different sample sizes and hidden nodes
ELM results for different sample sizes and hidden nodes

Fig. 14

Prediction graph for 160 test samples
Prediction graph for 160 test samples
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Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros
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