Design of a virtual sensor test platform for ICU patient glucose-insulin system using the HIL technique
DOI:
https://doi.org/10.33131/24222208.318Keywords:
Intensive Care Unit, Hardware-in-the-loop, Embedded systems, Glucose measurementAbstract
The traditional simulation In Silico (computational simulation) does not allow to recreate realistic environments. Tools like hardware-in-the-loop (HIL) allow the real-time simulation of the response of a system to disturbances. These tools come in handy for the study of the variations in glucose regulation for a patient in the Intensive Care Unit, such as hyperglycemia or hypoglycemia. In this investigation, a methodology was developed using HIL simulation technique to create a test platform for virtual sensors for the system patients' glucose – insulin in UCI. Also, state estimation techniques were employed, requiring the use of a communication structure to submit the system to real-time disturbances. Moreover, the work involved the development of an interface for the manipulation of the principal characteristics and parameters of both the model and the virtual sensors.
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References
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[3] B. M. Hanson, M. C. Levesley, K. Watterson, and P. G. Walker, “Hardware-in-the-loop-simulation of the cardiovascular system, with assist device testing application,” Med. Eng. Phys., vol. 29, no. 3, pp. 367–374, 2007.
[4] A. Alazmani et al., “Introducing a hardware-in-the-loop simulation of the cardiovascular system,” Proc. IEEE RAS EMBS Int. Conf. Biomed. Robot. Biomechatronics, pp. 153–158, 2012.
[5] C. Barker, M. Kwiatkowska, A. Mereacre, N. Paoletti, and A. Patan, “Hardware-in-the-loop simulation and energy optimization of cardiac pacemakers,” pp. 7188–7191, 2015.
[6] M. Bacic, “On hardware-in-the-loop simulation,” Proc. 44th IEEE Conf. Decis. Control, pp. 3194–3198, 2005.
[7] K. H. Ian T. Cameron, Process Modelling and Model Analysis, vol. 4, no. 2. 2001.
[8] C. Eberle and C. Ament, “Real-time state estimation and long-term model adaptation: A two-sided approach toward personalized diagnosis of glucose and insulin levels,” J. Diabetes Sci. Technol., vol. 6, no. 5, pp. 1148–1158, 2012.
[9] R. Gillis, C. C. Palerm, H. Zisser, L. Jovanovic, F. Doyle III, and D. E. Seborg, “Glucose Estimation and Prediction through Meal Responses Using Ambulatory Subject Data for Advisory Mode Model Predictive Control.,” J. diabetes Sci. Technol., vol. 1, no. 6, pp. 825–33, 2007.
[10] R. Sanchis, I. Peñarrocha, and P. Albertos, “Design of robust output predictors under scarce measurements with time-varying delays,” Automatica, vol. 43, no. 2, pp. 281–289, 2007.
[11] J. Mohd, N. H. Hoang, M. A. Hussain, and D. Dochain, “Review and classification of recent observers applied in chemical process systems,” Comput. Chem. Eng., vol. 76, pp. 27–41, 2015.
[12] J. G. Chase, G. M. Shaw, X. W. Wong, T. Lotz, J. Lin, and C. E. Hann, “Model-based glycaemic control in critical care-A review of the state of the possible,” Biomed. Signal Process. Control, vol. 1, no. 1, pp. 3–21, 2006.
[13] C. Eberle and C. Ament, “The Unscented Kalman Filter estimates the plasma insulin from glucose measurement,” BioSystems, vol. 103, no. 1, pp. 67–72, 2011.
[14] S. Casco, “Raspberry Pi , Arduino y Beaglebone Black Comparación y Aplicaciones,” vol. 1, pp. 4–8, 2014.
[15] E. D. Aguirre Mesa, D. A. Garcia Arango, y C. F. Henao Villa, “Diseño y simulación de un amplificador ‘Instrumental Operacional’ como apoyo en la lectura y transmisión de señales mioeléctricas por un solo canal con bajo ruido”, Revista CINTEX, vol. 22, núm. 1, pp. 11–23, 2017.
[16] J. E. Castano, J. Patino, y J. J. Espinosa, “Model identification for control of a distillation column”, en Robotics Symposium, 2011 IEEE IX Latin American and IEEE Colombian Conference on Automatic Control and Industry Applications (LARC), Bogotá, Colombia, 2011, pp. 1–5. DOI: 10.1109/LARC.2011.6086832
[17] V. Srdanovic et al., “Diseño y simulación de un sistema de medición de temperatura orientado hacia el aprendizaje aplicativo”, Revista CINTEX, vol. 19, pp. 9–22, 2014.
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Published
2018-12-31
How to Cite
Hernandez, C., Velez, D., & Isaza, J. A. (2018). Design of a virtual sensor test platform for ICU patient glucose-insulin system using the HIL technique. Revista CINTEX, 23(2), 61–75. https://doi.org/10.33131/24222208.318
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RESEARCH PAPERS
