Predicting the bladder urinary volume with a reabsorbed primitive urine model
With the rapid aging of the population, urination management is one of the challenges experienced in nursing homes. Although constrained devices, such as ultrasonic sensors, have been used for urination management, and they can sequentially measure urinary volume in the bladder, unconstrained method...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-06-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2021.1874679 |
| _version_ | 1797661131116904448 |
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| author | Hirota Taku Hamada Yuri Kaburagi Takashi Kurihara Yosuke |
| author_facet | Hirota Taku Hamada Yuri Kaburagi Takashi Kurihara Yosuke |
| author_sort | Hirota Taku |
| collection | DOAJ |
| description | With the rapid aging of the population, urination management is one of the challenges experienced in nursing homes. Although constrained devices, such as ultrasonic sensors, have been used for urination management, and they can sequentially measure urinary volume in the bladder, unconstrained methods to obtain urinary volume are needed. To accomplish such goals, a mathematical model is required that considers the nature of the bladder, especially reabsorption of the primitive urine. In this paper, we propose a model based on the primary delay system with five parameters, which are determined based on the absorption spectrum of urine that is obtained immediately after urination, through regression analysis. In the regression analysis, the values of the five parameters and the absorption spectrum of urine are objective and explanatory variables, respectively, and the partial regression coefficients are determined through a genetic algorithm. When the values of the five parameters are estimated using the absorption spectrum of urine immediately after urination, we can predict the next time series of the urinary volume in the bladder based on the model. Finally, the predicted urinary volume is corrected using a multitask Gaussian process and the final predicted urinary volume is obtained. We performed a series of experiments to evaluate the proposed method and calculated the error rate between the actual urinary volume and the urinary volume predicted using the proposed method at the time of urination. The mean error rate of the proposed method is 13.32%. |
| first_indexed | 2024-03-11T18:40:01Z |
| format | Article |
| id | doaj.art-a4556e788b3843099b08d397595f35a2 |
| institution | Directory Open Access Journal |
| issn | 1884-9970 |
| language | English |
| last_indexed | 2024-03-11T18:40:01Z |
| publishDate | 2021-06-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj.art-a4556e788b3843099b08d397595f35a22023-10-12T13:36:25ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702021-06-011422910.1080/18824889.2021.18746791874679Predicting the bladder urinary volume with a reabsorbed primitive urine modelHirota Taku0Hamada Yuri1Kaburagi Takashi2Kurihara Yosuke3Department of Industrial and Systems Engineering, Aoyama Gakuin UniversityDepartment of Industrial and Systems Engineering, Aoyama Gakuin UniversityDepartment of Natural Sciences, International Christian UniversityDepartment of Industrial and Systems Engineering, Aoyama Gakuin UniversityWith the rapid aging of the population, urination management is one of the challenges experienced in nursing homes. Although constrained devices, such as ultrasonic sensors, have been used for urination management, and they can sequentially measure urinary volume in the bladder, unconstrained methods to obtain urinary volume are needed. To accomplish such goals, a mathematical model is required that considers the nature of the bladder, especially reabsorption of the primitive urine. In this paper, we propose a model based on the primary delay system with five parameters, which are determined based on the absorption spectrum of urine that is obtained immediately after urination, through regression analysis. In the regression analysis, the values of the five parameters and the absorption spectrum of urine are objective and explanatory variables, respectively, and the partial regression coefficients are determined through a genetic algorithm. When the values of the five parameters are estimated using the absorption spectrum of urine immediately after urination, we can predict the next time series of the urinary volume in the bladder based on the model. Finally, the predicted urinary volume is corrected using a multitask Gaussian process and the final predicted urinary volume is obtained. We performed a series of experiments to evaluate the proposed method and calculated the error rate between the actual urinary volume and the urinary volume predicted using the proposed method at the time of urination. The mean error rate of the proposed method is 13.32%.http://dx.doi.org/10.1080/18824889.2021.1874679urinary tract infectionurinary volumeabsorption spectrumgenetic algorithmmultitask gaussian process |
| spellingShingle | Hirota Taku Hamada Yuri Kaburagi Takashi Kurihara Yosuke Predicting the bladder urinary volume with a reabsorbed primitive urine model SICE Journal of Control, Measurement, and System Integration urinary tract infection urinary volume absorption spectrum genetic algorithm multitask gaussian process |
| title | Predicting the bladder urinary volume with a reabsorbed primitive urine model |
| title_full | Predicting the bladder urinary volume with a reabsorbed primitive urine model |
| title_fullStr | Predicting the bladder urinary volume with a reabsorbed primitive urine model |
| title_full_unstemmed | Predicting the bladder urinary volume with a reabsorbed primitive urine model |
| title_short | Predicting the bladder urinary volume with a reabsorbed primitive urine model |
| title_sort | predicting the bladder urinary volume with a reabsorbed primitive urine model |
| topic | urinary tract infection urinary volume absorption spectrum genetic algorithm multitask gaussian process |
| url | http://dx.doi.org/10.1080/18824889.2021.1874679 |
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