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- W2734479283 abstract "Insulin sensitivity is known to be an endogenous time-varying parameter in glucose metabolism. A real-time available quantification of insulin sensitivity is advantageous, if used in the adaption of controller designs for the artificial pancreas. In this paper, we present a new approach towards real-time estimation of insulin sensitivity in type-1 diabetes. The estimation of insulin sensitivity is investigated while a carbohydrate uptake disturbance takes place at the same time. A novel reduced-order filtering approach is presented, that is based on empirical gramian based observability analysis and subsequent model order-reduction with a Petrov-Galerkin projection. Based on the reduced-order model, an Unscented Kalman filter is developed to estimate time-varying insulin sensitivity as an additional state. The filter is verified in in silico experiments with a detailed model that was validated in animal experiments." @default.
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- W2734479283 date "2017-05-01" @default.
- W2734479283 modified "2023-09-24" @default.
- W2734479283 title "Reduced-order filtering for insulin sensitivity estimation under external disturbances" @default.
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- W2734479283 doi "https://doi.org/10.23919/acc.2017.7963156" @default.
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