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• W2024272614 endingPage "72" @default.
• W2024272614 startingPage "67" @default.
• W2024272614 abstract "Understanding the simultaneous interaction within the glucose and insulin homeostasis in real-time is very important for clinical treatment as well as for research issues. Until now only plasma glucose concentrations can be measured in real-time. To support a secure, effective and rapid treatment e.g. of diabetes a real-time estimation of plasma insulin would be of great value. A novel approach using an Unscented Kalman Filter that provides an estimate of the current plasma insulin concentration is presented, which operates on the measurement of the plasma glucose and Bergman's Minimal Model of the glucose insulin homeostasis. We can prove that process observability is obtained in this case. Hence, a successful estimator design is possible. Since the process is nonlinear we have to consider estimates that are not normally distributed. The symmetric Unscented Kalman Filter (UKF) will perform best compared to other estimator approaches as the Extended Kalman Filter (EKF), the simplex Unscented Kalman Filter (UKF), and the Particle Filter (PF). The symmetric UKF algorithm is applied to the plasma insulin estimation. It shows better results compared to the direct (open loop) estimation that uses a model of the insulin subsystem." @default.
• W2024272614 created "2016-06-24" @default.
• W2024272614 creator A5063932190 @default.
• W2024272614 creator A5071277894 @default.
• W2024272614 date "2011-01-01" @default.
• W2024272614 modified "2023-10-03" @default.
• W2024272614 title "The Unscented Kalman Filter estimates the plasma insulin from glucose measurement" @default.
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• W2024272614 doi "https://doi.org/10.1016/j.biosystems.2010.09.012" @default.
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• W2024272614 hasPublicationYear "2011" @default.
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