Modelling of glucose-insulin dynamics from low sampled data

Tinna Björk Aradóttir, Dimitri Boiroux, Henrik Bengtsson, Niels Kjølstad Poulsen

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In this paper we focus on modelling the glucose-insulin dynamics in the human body for the purpose of controlling the glucose level. Due to the fast dynamics in the glucoseinsulin system compared to the natural sampling period (24 h) in a clinical situation, the model structure has to be adapted adequately. This results in a reduced order model with a nonlinear output relation. The development of the estimation methodology is based on a simulation study with a continuous time model. The resulting model structure is used for estimating the parameters of the non-linear system, representing the slow dynamics observed from the slow and sparse sampled clinical data.
Original languageEnglish
Title of host publicationProceedings of 18th IFAC Symposium on System Identification
Publication date2018
Publication statusPublished - 2018
Event18th IFAC Symposium on System Identification - AlbaNova University Center, Stockholm, Sweden
Duration: 9 Jul 201811 Jul 2018
Conference number: 18


Conference18th IFAC Symposium on System Identification
LocationAlbaNova University Center


  • System identification
  • Reduced order models
  • Glucose-insulin dynamics
  • Diabetes control


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