Please use this identifier to cite or link to this item: http://ir.futminna.edu.ng:8080/jspui/handle/123456789/18796
Title: Towards the Development of Intelligent Insulin Injection Controller For Diabetic Patients
Authors: Adedigba, Adeyinka Peace
Zubair, Abdul Razak
Aibinu, Abiodun Musa
Adeshina, Steve A.
Okubadejo, Olumide
Folorunso, Taliha Abiodun
Keywords: Adaptive Controller Technique
Artificial intelligence
Diabetic Mellitus
Feedback control
Model Predictive Controller
Palumbo
PID Controller
Reinforcement Learning
Issue Date: 10-Dec-2019
Publisher: 2019 15th International Conference on Electronics, Computer and Computation (ICECCO)
Citation: Adedigba, A. P., Zubair, A. R., Aibinu, A. M., Adeshina, S. A., Okubadejo, O., & Folorunso, T. A. (2019, December). Towards the Development of Intelligent Insulin Injection Controller For Diabetic Patients. In 2019 15th International Conference on Electronics, Computer and Computation (ICECCO) (pp. 1-6). IEEE.
Abstract: Diabetes Mellitus (DM) is a disease of the glucose insulin regulatory system where the insulin producing beta-cells has been damaged thereby producing none to very little insulin leaving the body with no means of regulating glucose. DM has high socioeconomic costs because it needs long term monitoring and individual care to prevent or decrease complications. Uncontrolled or poorly controlled diabetes lead to evolution or development of microvascular and macrovascular complications. It has been shown that adequate or even tight glycaemic control can prevent or delay complications and finally can reduce these complications. One of this glycaemic control is insulin therapy, meanwhile, non-adherence to the therapy due to its sever pain is prevalent among patients. In this paper, a review of research efforts towards the development of automatic insulin injection from control engineering perspective is presented. The reviewed techniques are basically closed loop approach, which include PID controllers, Model Predictive Controllers and Adaptive Controller techniques using machine learning approaches.
URI: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/18796
Appears in Collections:Mechatronics Engineering

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