Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning Algorithm
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 pap...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2023-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2022.2.0041 |
| _version_ | 1797688743973355520 |
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| author | Zhu Haiqin Liang Hao Xiao Fulai Wang Gepeng Hussain Rifat |
| author_facet | Zhu Haiqin Liang Hao Xiao Fulai Wang Gepeng Hussain Rifat |
| author_sort | Zhu Haiqin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T01:36:17Z |
| format | Article |
| id | doaj.art-6e146d16b8d64783b48693cd8240c84c |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-12T01:36:17Z |
| publishDate | 2023-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-6e146d16b8d64783b48693cd8240c84c2023-09-11T07:01:08ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562023-01-018155957210.2478/amns.2022.2.0041Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning AlgorithmZhu Haiqin0Liang Hao1Xiao Fulai2Wang Gepeng3Hussain Rifat41Yantai Nanshan University, Yantai, 265713, China1Yantai Nanshan University, Yantai, 265713, China2Shandong Nanshan Science and Technology Research Institute, Yantai, 265713, China3Nanshan zhonggaoxie International Training Center, Yantai, 265713, China4College of Administrative Sciences, Applied Science University, BahrainSleeping 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.https://doi.org/10.2478/amns.2022.2.0041relmlying position pressure imagemultiple feature valueslying position recognition34a34 |
| spellingShingle | Zhu Haiqin Liang Hao Xiao Fulai Wang Gepeng Hussain Rifat Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning Algorithm Applied Mathematics and Nonlinear Sciences relm lying position pressure image multiple feature values lying position recognition 34a34 |
| title | Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning Algorithm |
| title_full | Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning Algorithm |
| title_fullStr | Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning Algorithm |
| title_full_unstemmed | Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning Algorithm |
| title_short | Pressure Image Recognition of Lying Positions Based on Multi-feature value Regularized Extreme Learning Algorithm |
| title_sort | pressure image recognition of lying positions based on multi feature value regularized extreme learning algorithm |
| topic | relm lying position pressure image multiple feature values lying position recognition 34a34 |
| url | https://doi.org/10.2478/amns.2022.2.0041 |
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