ArticleProceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control2015
Empirical Dynamic Model Identification for Blood-Glucose Dynamics in Response to Physical Activity.
Article in Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients.Heliyon · 2024Article
- Adaptive Control of an Artificial Pancreas Using Model Identification, Adaptive Postprandial Insulin Delivery, and Heart Rate and Accelerometry as Control Inputs.Journal of diabetes science and technology · 2019Article
- Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes.Journal of diabetes science and technology · 2019Article
- Reducing Glucose Variability Due to Meals and Postprandial Exercise in T1DM Using Switched LPV Control: In Silico Studies.Journal of diabetes science and technology · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In this paper, the dynamic response of blood glucose concentration in response to physical activity of people with Type 1 Diabetes Mellitus (T1DM) is captured by subspace identification methods. Activity (input) and subcutaneous blood glucose measurements (output) are employed to construct a personalized prediction model through semi-definite programming. The model is calibrated and subsequently validated with non-overlapping data sets from 15 T1DM subjects. This preliminary clinical evaluation reveals the underlying linear dynamics between blood glucose concentration and physical activity. These types of models can enhance our capabilities of achieving tighter blood glucose control and early detection of hypoglycemia for people with T1DM.
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Registered trials
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