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<p>Sleeping postures are one of the indicators for judging sleep quality and preventing sudden diseases. The sleeping postures not only affect people’s sleep quality but also has great significance for the diagnosis of apnea syndrome and bedsores. To realize and recognize the laying positions, this paper researches the regularized extreme learning (RELM) algorithm to analyze the pressure due to lying positions. Based on this algorithm first, the array pressure sensor is used to obtain the back lying posture pressure image, and the image is pre-processed to complete the extraction of multiple feature values (Geometric features, Energy features, and Colour features). Second, the multi-feature values are normalized and finally, these multi-feature values are trained and predicted by the RELM algorithm. In concluding this, the accuracy of lying posture recognition was the highest, achieving 98.75 percent, this is when 1120 datasets of feature values were used as training data and 160 sets as test data while the hidden nodes were 80. RELM algorithm can overcome the problems of extreme learning (ELM) algorithm, such as slow learning speed and local minimum value, and so on. Therefore, this method can be applied in the scenarios of lying posture recognition.</p>
</abstract>

Sleeping postures are one of the indicators for judging sleep quality and preventing sudden diseases. The sleeping postures not only affect people’s sleep quality but also has great significance for the diagnosis of apnea syndrome and bedsores. To realize and recognize the laying positions, this paper researches the regularized extreme learning (RELM) algorithm to analyze the pressure due to lying positions. Based on this algorithm first, the array pressure sensor is used to obtain the back lying posture pressure image, and the image is pre-processed to complete the extraction of multiple feature values (Geometric features, Energy features, and Colour features). Second, the multi-feature values are normalized and finally, these multi-feature values are trained and predicted by the RELM algorithm. In concluding this, the accuracy of lying posture recognition was the highest, achieving 98.75 percent, this is when 1120 datasets of feature values were used as training data and 160 sets as test data while the hidden nodes were 80. RELM algorithm can overcome the problems of extreme learning (ELM) algorithm, such as slow learning speed and local minimum value, and so on. Therefore, this method can be applied in the scenarios of lying posture recognition.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Sleeping postures are one of the indicators for judging sleep quality and preventing sudden diseases. The sleeping postures not only affect people’s sleep...

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

Data publikacji: 15 lip 2022

Zakres stron: 559 - 572

Otrzymano: 07 lut 2022

Przyjęty: 05 kwi 2022

DOI: https://doi.org/10.2478/amns.2022.2.0041

Słowa kluczowe
RELM, Lying position pressure image, Multiple feature values, Lying position recognition

© 2023 Haiqin Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Data publikacji: 15 lip 2022

Zakres stron: 559 - 572

Otrzymano: 07 lut 2022

Przyjęty: 05 kwi 2022

DOI: https://doi.org/10.2478/amns.2022.2.0041

Słowa kluczoweRELM, Lying position pressure image, Multiple feature values, Lying position recognition

© 2023 Haiqin Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
RELM, Lying position pressure image, Multiple feature values, Lying position recognition