On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing
Type 1 Diabetes Mellitus (T1DM) is one of the most adverse diseases in the modern era; its treatment is mainly based on exogenous insulin injections. The scientific community has formulated strategies to improve insulin supply using state-of-the-art technology. Therefore, this article develops a mul...
- Autores:
-
Rios, Yuliana
Garcia-Rodriguez, Julio
Sanchez, Edgar
Alanis, Alma
Ruizvelazquez, Eduardo
Pardo-Garcia, Aldo
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12426
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12426
- Palabra clave:
- Glucose; Hypoglycemia;
Insulin Dependent Diabetes Mellitus
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing |
| title |
On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing |
| spellingShingle |
On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing Glucose; Hypoglycemia; Insulin Dependent Diabetes Mellitus LEMB |
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On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing |
| title_full |
On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing |
| title_fullStr |
On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing |
| title_full_unstemmed |
On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing |
| title_sort |
On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing |
| dc.creator.fl_str_mv |
Rios, Yuliana Garcia-Rodriguez, Julio Sanchez, Edgar Alanis, Alma Ruizvelazquez, Eduardo Pardo-Garcia, Aldo |
| dc.contributor.author.none.fl_str_mv |
Rios, Yuliana Garcia-Rodriguez, Julio Sanchez, Edgar Alanis, Alma Ruizvelazquez, Eduardo Pardo-Garcia, Aldo |
| dc.subject.keywords.spa.fl_str_mv |
Glucose; Hypoglycemia; Insulin Dependent Diabetes Mellitus |
| topic |
Glucose; Hypoglycemia; Insulin Dependent Diabetes Mellitus LEMB |
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LEMB |
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Type 1 Diabetes Mellitus (T1DM) is one of the most adverse diseases in the modern era; its treatment is mainly based on exogenous insulin injections. The scientific community has formulated strategies to improve insulin supply using state-of-the-art technology. Therefore, this article develops a multi-age glycemic control scheme, which can be implemented in an Artificial Pancreas (AP) device to enhance diabetics treatment. The procedure is based on the implementation of a neuro-fuzzy inverse optimal control (NFIOC) algorithm on the Texas Instrument LAUNCHXLF28069M development board; this controller communicates with the Uva/Padova simulator for diabetics' patients of different ages under predefined meal protocols running on a Personal Computer (PC). The novelty lies in the proposed NFIOC capability to regulate glucose within safe levels for virtual populations of 10 adults, 10 adolescents and, 10 children. © 2022 AECE |
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2022 |
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2022 |
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2023-07-25T12:10:03Z |
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2023-07-25T12:10:03Z |
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2023 |
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Rios, Y., Garcia-Rodriguez, J., Sanchez, E., Alanis, A., Ruiz-Velazquez, E., & Pardo-Garcia, A. (2022). On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing. Advances in Electrical & Computer Engineering, 22(3). |
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https://hdl.handle.net/20.500.12585/12426 |
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10.4316/AECE.2022.03001 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Rios, Y., Garcia-Rodriguez, J., Sanchez, E., Alanis, A., Ruiz-Velazquez, E., & Pardo-Garcia, A. (2022). On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing. Advances in Electrical & Computer Engineering, 22(3). 10.4316/AECE.2022.03001 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12426 |
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eng |
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eng |
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Rios, Yuliana0dbdd108-59ee-4e22-85cd-3de4922f37b4Garcia-Rodriguez, Julio56c041f4-bcc1-44ef-8e3c-a8ec6ea17e4eSanchez, Edgardde5501b-9920-4b9c-8ae4-6378407a487cAlanis, Almae3a9155e-04ad-4ce3-972e-03923c3305beRuizvelazquez, Eduardodafb89f7-ed2f-42a4-865a-b5b932b6c2bePardo-Garcia, Aldo3e44cda3-c4d1-46d1-8551-af25e3f4caa72023-07-25T12:10:03Z2023-07-25T12:10:03Z20222023Rios, Y., Garcia-Rodriguez, J., Sanchez, E., Alanis, A., Ruiz-Velazquez, E., & Pardo-Garcia, A. (2022). On Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testing. Advances in Electrical & Computer Engineering, 22(3).https://hdl.handle.net/20.500.12585/1242610.4316/AECE.2022.03001Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarType 1 Diabetes Mellitus (T1DM) is one of the most adverse diseases in the modern era; its treatment is mainly based on exogenous insulin injections. The scientific community has formulated strategies to improve insulin supply using state-of-the-art technology. Therefore, this article develops a multi-age glycemic control scheme, which can be implemented in an Artificial Pancreas (AP) device to enhance diabetics treatment. The procedure is based on the implementation of a neuro-fuzzy inverse optimal control (NFIOC) algorithm on the Texas Instrument LAUNCHXLF28069M development board; this controller communicates with the Uva/Padova simulator for diabetics' patients of different ages under predefined meal protocols running on a Personal Computer (PC). The novelty lies in the proposed NFIOC capability to regulate glucose within safe levels for virtual populations of 10 adults, 10 adolescents and, 10 children. © 2022 AECEapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Advances in Electrical and Computer EngineeringOn Board Neuro Fuzzy Inverse Optimal Control for Type 1 Diabetes Mellitus Treatment: In-Silico Testinginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Glucose; Hypoglycemia;Insulin Dependent Diabetes MellitusLEMBCartagena de IndiasInsulin Dependent Diabetes MellitusYang, W., Dall, T.M., Beronjia, K., Lin, J., Semilla, A.P., Chakrabarti, R., Hogan, P.F., (...), Petersen, M.P. Economic costs of diabetes in the U.S. in 2017 (2018) Diabetes Care, 41 (5), pp. 917-928. Cited 1403 times. http://care.diabetesjournals.org/content/41/5/917.full-text.pdf doi: 10.2337/dci18-0007Kim, S. Burden of hospitalizations primarily due to uncontrolled diabetes: Implications of inadequate primary health care in the United States (2007) Diabetes Care, 30 (5), pp. 1281-1282. Cited 99 times. http://care.diabetesjournals.org/cgi/reprint/30/5/1281 doi: 10.2337/dc06-2070Brown, J.B., Pedula, K.L., Bakst, A.W. The progressive cost of complications in type 2 diabetes mellitus (1999) Archives of Internal Medicine, 159 (16), pp. 1873-1880. Cited 197 times. doi: 10.1001/archinte.159.16.1873Yeh, H.-C., Brown, T.T., Maruthur, N., Ranasinghe, P., Berger, Z., Suh, Y.D., Wilson, L.M., (...), Golden, S.H. Comparative effectiveness and safety of methods of insulin delivery and glucose monitoring for diabetes mellitus: A systematic review and meta-analysis (2012) Annals of Internal Medicine, 157 (5), pp. 336-347. Cited 405 times. http://annals.org/data/Journals/AIM/24808/0000605-201209040-00006.pdf doi: 10.7326/0003-4819-157-5-201209040-00508Rios, Y.Y., García-Rodríguez, J.A., Sánchez, O.D., Sanchez, E.N., Alanis, A.Y., Ruiz-Velázquez, E., Arana-Daniel, N. Inverse Optimal Control Using A Neural Multi-Step Predictor for T1DM Treatment (2018) Proceedings of the International Joint Conference on Neural Networks, 2018-July, art. no. 8489197. Cited 9 times. ISBN: 978-150906014-6 doi: 10.1109/IJCNN.2018.8489197Alanis, A.Y., Rios, Y., García-Rodríguez, J.A., Sanchez, E.N., Ruiz-Velázquez, E., Garcia, A.P. Neuro-fuzzy inverse optimal control incorporating a multistep predictor as applied to T1DM patients (2020) Control Applications for Biomedical Engineering Systems, pp. 1-24. Cited 2 times. https://www.sciencedirect.com/book/9780128174616/control-applications-for-biomedical-engineering-systems ISBN: 978-012817461-6 doi: 10.1016/B978-0-12-817461-6.00001-9Rios, Y.Y., Garcia-Rodriguez, J.A., Sanchez, E.N., Alanis, A.Y., Ruiz-Velazquez, E. Rapid Prototyping of Neuro-Fuzzy Inverse Optimal Control as Applied to T1DM Patients (Open Access) (2018) 2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018, art. no. 8625241. Cited 7 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8609898 ISBN: 978-153864625-0 doi: 10.1109/LA-CCI.2018.8625241Pes, P., Herrero, P., Reddy, M., Xenou, M., Oliver, N., Johnston, D., Toumazou, C., (...), Georgiou, P. An advanced bolus calculator for type 1 diabetes: System architecture and usability results (2016) IEEE Journal of Biomedical and Health Informatics, 20 (1), art. no. 7174940, pp. 11-17. 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Cited 171 times. link.springer.de/link/service/journals/00125/index.htm doi: 10.1007/s00125-016-4022-4Kropff, J., Del Favero, S., Place, J., Toffanin, C., Visentin, R., Monaro, M., Messori, M., (...), Magni, L. 2 month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: A randomised crossover trial (2015) The Lancet Diabetes and Endocrinology, 3 (12), pp. 939-947. Cited 185 times. http://www.journals.elsevier.com/the-lancet-diabetes-and-endocrinology doi: 10.1016/S2213-8587(15)00335-6Quintero-Manriquez, E., Sanchez, E.N., Harley, R.G., Li, S., Felix, R.A. Neural Inverse Optimal Control Implementation for Induction Motors via Rapid Control Prototyping (Open Access) (2019) IEEE Transactions on Power Electronics, 34 (6), art. no. 8464291, pp. 5981-5992. Cited 23 times. https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=63 doi: 10.1109/TPEL.2018.2870159Gurubel, K.J., Sanchez, E.N., Coronado-Mendoza, A., Zuniga-Grajeda, V., Sulbaran-Rangel, B., Breton-Deval, L. Inverse optimal neural control via passivity approach for nonlinear anaerobic bioprocesses with biofuels production (Open Access) (2019) Optimal Control Applications and Methods, 40 (5), pp. 848-858. Cited 5 times. http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1514 doi: 10.1002/oca.2513Chan, V.M., Hernandez-Vargas, E.A., Sanchez, E.N. Neural inverse optimal control applied to design therapeutic options for patients with COVID-19 (2021) Proceedings of the International Joint Conference on Neural Networks, 2021-July. Cited 2 times. ISBN: 978-073813366-9 doi: 10.1109/IJCNN52387.2021.9534240Chen, T., Babanin, A., Muhammad, A., Chapron, B., Chen, C. Modified evolved bat algorithm of fuzzy optimal control for complex nonlinear systems (Open Access) (2020) Romanian Journal of Information Science and Technology, 23 (T), pp. T28-T40. Cited 50 times. http://www.romjist.ro/full-texts/paper672.pdfKarahoca, A., Karahoca, D., Kara, A. Diagnosis of diabetes by using adaptive neuro fuzzy inference systems (2009) ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, art. no. 5379497. Cited 11 times. ISBN: 978-142443428-2 doi: 10.1109/ICSCCW.2009.5379497Geman, O., Chiuchisan, I., Toderean, R. Application of Adaptive Neuro-Fuzzy Inference System for diabetes classification and prediction (Open Access) (2017) 2017 E-Health and Bioengineering Conference, EHB 2017, art. no. 7995505, pp. 639-642. Cited 30 times. ISBN: 978-153860358-1 doi: 10.1109/EHB.2017.7995505Lekkas, S., Mikhailov, L. Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases (Open Access) (2010) Artificial Intelligence in Medicine, 50 (2), pp. 117-126. Cited 76 times. doi: 10.1016/j.artmed.2010.05.007Nath, A., Dey, R., Balas, V.E. Closed loop blood glucose regulation of type 1 diabetic patient using Takagi-Sugeno fuzzy logic control (2018) Advances in Intelligent Systems and Computing, 634, pp. 286-296. Cited 10 times. http://www.springer.com/series/11156 ISBN: 978-331962523-2 doi: 10.1007/978-3-319-62524-9_23Trevitt, S., Simpson, S., Wood, A. Artificial Pancreas Device Systems for the Closed-Loop Control of Type 1 Diabetes: What Systems Are in Development? (2016) Journal of Diabetes Science and Technology, 10 (3), pp. 714-723. Cited 127 times. http://dst.sagepub.com/content/by/year doi: 10.1177/1932296815617968Dalla Man, C., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., Cobelli, C. The UVA/PADOVA type 1 diabetes simulator: New features (2014) Journal of Diabetes Science and Technology, 8 (1), pp. 26-34. Cited 497 times. doi: 10.1177/1932296813514502Kovatchev, B.P., Breton, M., Dalla Man, C., Cobelli, C. In silico preclinical trials: A proof of concept in closed-loop control of type 1 diabetes (2009) Journal of Diabetes Science and Technology, 3 (1), pp. 44-55. Cited 595 times. http://dst.sagepub.com/content/by/year doi: 10.1177/193229680900300106Alanis, A.Y., Sanchez, E.N., Loukianov, A.G. Discrete-time adaptive backstepping nonlinear control via high-order neural networks (Open Access) (2007) IEEE Transactions on Neural Networks, 18 (4), pp. 1185-1195. Cited 151 times. doi: 10.1109/TNN.2007.899170Chen, P.-A., Chang, L.-C., Chang, F.-J. Reinforced recurrent neural networks for multi-step-ahead flood forecasts (Open Access) (2013) Journal of Hydrology, 497, pp. 71-79. Cited 108 times. doi: 10.1016/j.jhydrol.2013.05.038Ohsawa, T., Bloch, A.M., Leok, M. Discrete Hamilton-Jacobi theory and discrete optimal control (2010) Proceedings of the IEEE Conference on Decision and Control, art. no. 5717665, pp. 5438-5443. Cited 39 times. ISBN: 978-142447745-6 doi: 10.1109/CDC.2010.5717665Al-Tamimi, A., Lewis, F.L., Abu-Khalaf, M. Discrete-time nonlinear HJB solution using approximate dynamic programming: Convergence proof (2008) IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 38 (4), pp. 943-949. Cited 821 times. doi: 10.1109/TSMCB.2008.926614Sanchez, E.N., Ornelas-Tellez, F. Discrete-time inverse optimal control for nonlinear systems (Open Access) (2017) Discrete-Time Inverse Optimal Control for Nonlinear Systems, pp. 1-232. Cited 38 times. http://www.tandfebooks.com/doi/book/10.1201/b14779 ISBN: 978-146658088-6; 978-146658087-9 doi: 10.1201/b14779Li, W., Todorov, E., Liu, D. Inverse optimality design for biological movement systems (2011) IFAC Proceedings Volumes (IFAC-PapersOnline), 44 (1 PART 1), pp. 9662-9667. Cited 25 times. http://www.ifac-papersonline.net/browser?browse=c ISBN: 978-390266193-7 doi: 10.3182/20110828-6-IT-1002.00877Ornelas, F., Sanchez, E.N., Loukianov, A.G. Discrete-time nonlinear systems inverse optimal control: A control Lyapunov function approach (Open Access) (2011) Proceedings of the IEEE International Conference on Control Applications, art. no. 6044461, pp. 1431-1436. Cited 33 times. ISBN: 978-145771062-9 doi: 10.1109/CCA.2011.6044461Ornelas-Tellez, F., Sanchez, E.N., Loukianov, A.G., Navarro-Lopez, E.M. Speed-gradient inverse optimal control for discrete-time nonlinear systems (2011) Proceedings of the IEEE Conference on Decision and Control, art. no. 6160374, pp. 290-295. Cited 27 times. ISBN: 978-161284800-6 doi: 10.1109/CDC.2011.6160374Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients) (Open Access) (2005) Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients), pp. 1-1331. 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