{"title":"A Minimal Model for Type-1 DM Patients: Meal and Exercise Adaptation","authors":"Abishek Chandrasekhar, Radhakant Padhi","doi":"10.1016/j.ifacol.2024.05.009","DOIUrl":null,"url":null,"abstract":"<div><p>Mathematical Modeling of glucose-insulin dynamics of Type-1 Diabetic Mellitus (T1DM) patients is an essential component of designing and developing Artificial Pancreas Systems. These model parameters, which exhibit significant inter-patient variability, are identified for each individual T1DM patient through standard tolerance tests. However, in addition to the inter-patient variability, each patient's model parameters vary according to the circadian rhythm. Therefore, the blood glucose response to a meal is different at breakfast when compared to lunch and dinner. In addition to this, the glucose-insulin dynamics vary when the T1DM patient exercises or during any physical activity. To account for these intra-patient variabilities and the variability due to exercise, a neuro-adaptive learning scheme is proposed in this work. The uncertainties are approximated as a product of a weight and a meaningful basis function. The model uncertainties are learned during meals and idle activity, whereas exercise learning requires an announcement from the patient and is only learned when the patient is exercising. This neuroadaptive learning scheme can prove to be of vital importance in designing model-based control laws for blood glucose regulation in Type-1 Diabetic patients.</p></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405896324000090/pdf?md5=c8ce986a6bdb8673a9fc7961a1d54b25&pid=1-s2.0-S2405896324000090-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IFAC-PapersOnLine","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405896324000090","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Mathematical Modeling of glucose-insulin dynamics of Type-1 Diabetic Mellitus (T1DM) patients is an essential component of designing and developing Artificial Pancreas Systems. These model parameters, which exhibit significant inter-patient variability, are identified for each individual T1DM patient through standard tolerance tests. However, in addition to the inter-patient variability, each patient's model parameters vary according to the circadian rhythm. Therefore, the blood glucose response to a meal is different at breakfast when compared to lunch and dinner. In addition to this, the glucose-insulin dynamics vary when the T1DM patient exercises or during any physical activity. To account for these intra-patient variabilities and the variability due to exercise, a neuro-adaptive learning scheme is proposed in this work. The uncertainties are approximated as a product of a weight and a meaningful basis function. The model uncertainties are learned during meals and idle activity, whereas exercise learning requires an announcement from the patient and is only learned when the patient is exercising. This neuroadaptive learning scheme can prove to be of vital importance in designing model-based control laws for blood glucose regulation in Type-1 Diabetic patients.
期刊介绍:
All papers from IFAC meetings are published, in partnership with Elsevier, the IFAC Publisher, in theIFAC-PapersOnLine proceedings series hosted at the ScienceDirect web service. This series includes papers previously published in the IFAC website.The main features of the IFAC-PapersOnLine series are: -Online archive including papers from IFAC Symposia, Congresses, Conferences, and most Workshops. -All papers accepted at the meeting are published in PDF format - searchable and citable. -All papers published on the web site can be cited using the IFAC PapersOnLine ISSN and the individual paper DOI (Digital Object Identifier). The site is Open Access in nature - no charge is made to individuals for reading or downloading. Copyright of all papers belongs to IFAC and must be referenced if derivative journal papers are produced from the conference papers. All papers published in IFAC-PapersOnLine have undergone a peer review selection process according to the IFAC rules.