Volume estimation of fluid intake using regression models
Abstract Monitoring of water intake is critical for managing the health and wellness of individuals with various health conditions, including young children, sick adults, the elderly, and individuals seeking better weight control. The research presented in this paper studies the use of different reg...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-10-01
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| Series: | Journal of Engineering and Applied Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44147-023-00283-9 |
| _version_ | 1797559411683622912 |
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| author | E. A. Hassan A. A. Morsy |
| author_facet | E. A. Hassan A. A. Morsy |
| author_sort | E. A. Hassan |
| collection | DOAJ |
| description | Abstract Monitoring of water intake is critical for managing the health and wellness of individuals with various health conditions, including young children, sick adults, the elderly, and individuals seeking better weight control. The research presented in this paper studies the use of different regression methods to estimate water intake using wireless surface electromyography (sEMG). The advantage of using regression is that it can provide more consistent values for different swallow volumes. In addition, the setup reported in this research employs a less controlled environment, providing stronger evidence of the practical feasibility of the used setup. Neural networks-based regression achieved an R 2 of 0.99 and a root-mean-squared error of 0.14 and 0.08 after feature selection. The relative immunity of sEMG as a sensing technique and the accuracy levels achieved with the used mobile sEMG device can provide a robust system for volume estimation of fluid intake in real-world situations. |
| first_indexed | 2024-03-10T17:44:59Z |
| format | Article |
| id | doaj.art-8c223b11e808457f947e9a68ab08fa80 |
| institution | Directory Open Access Journal |
| issn | 1110-1903 2536-9512 |
| language | English |
| last_indexed | 2024-03-10T17:44:59Z |
| publishDate | 2023-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj.art-8c223b11e808457f947e9a68ab08fa802023-11-20T09:33:33ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122023-10-0170111510.1186/s44147-023-00283-9Volume estimation of fluid intake using regression modelsE. A. Hassan0A. A. Morsy1Biomedical Engineering Department, Cairo UniversityBiomedical Engineering Department, Cairo UniversityAbstract Monitoring of water intake is critical for managing the health and wellness of individuals with various health conditions, including young children, sick adults, the elderly, and individuals seeking better weight control. The research presented in this paper studies the use of different regression methods to estimate water intake using wireless surface electromyography (sEMG). The advantage of using regression is that it can provide more consistent values for different swallow volumes. In addition, the setup reported in this research employs a less controlled environment, providing stronger evidence of the practical feasibility of the used setup. Neural networks-based regression achieved an R 2 of 0.99 and a root-mean-squared error of 0.14 and 0.08 after feature selection. The relative immunity of sEMG as a sensing technique and the accuracy levels achieved with the used mobile sEMG device can provide a robust system for volume estimation of fluid intake in real-world situations.https://doi.org/10.1186/s44147-023-00283-9Fluid volume intakesEMGRegression models |
| spellingShingle | E. A. Hassan A. A. Morsy Volume estimation of fluid intake using regression models Journal of Engineering and Applied Science Fluid volume intake sEMG Regression models |
| title | Volume estimation of fluid intake using regression models |
| title_full | Volume estimation of fluid intake using regression models |
| title_fullStr | Volume estimation of fluid intake using regression models |
| title_full_unstemmed | Volume estimation of fluid intake using regression models |
| title_short | Volume estimation of fluid intake using regression models |
| title_sort | volume estimation of fluid intake using regression models |
| topic | Fluid volume intake sEMG Regression models |
| url | https://doi.org/10.1186/s44147-023-00283-9 |
| work_keys_str_mv | AT eahassan volumeestimationoffluidintakeusingregressionmodels AT aamorsy volumeestimationoffluidintakeusingregressionmodels |