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| Content Provider | The American Society of Mechanical Engineers (ASME) Digital Collection |
|---|---|
| Author | Xie, Jinyu Wang, Qian |
| Copyright Year | 2016 |
| Abstract | Physical activity is an important physiological information which should be taken into account by artificial pancreas to achieve optimal control of blood glucose in Type 1 Diabetes patients. An accurate glucose dynamic model with physical activity as an additional input is highly desirable for the next generation artificial pancreas. In this paper, we present a nonlinear data-driven model that captures both the insulin-independent and -dependent effect of physical activity, especially the prolonged effect of physical activity on insulin sensitivity that can last 24–48 hours post exercise. The model was identified and validated using data sets generated by a physiological glucose-exercise model under a clinical training protocol. Compared to modeling the effect of physical activity as a linear additive term only in a glucose dynamic equation, the proposed nonlinear model showed significant improvement of prediction accuracy in all three metrics, particularly in large prediction horizons (P < 0.05). Further investigation in time-series data indicates that the improvement mainly resulted from the better prediction of glucose around the first meal time after exercise (6 to 8 hours after the meal was taken). |
| Sponsorship | Dynamic Systems and Control Division |
| File Format | |
| ISBN | 9780791850695 |
| DOI | 10.1115/DSCC2016-9742 |
| Volume Number | Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation |
| Conference Proceedings | ASME 2016 Dynamic Systems and Control Conference |
| Language | English |
| Publisher Date | 2016-10-12 |
| Publisher Place | Minneapolis, Minnesota, USA |
| Access Restriction | Subscribed |
| Subject Keyword | Blood glucose Accounting Dynamics (mechanics) Optimal control Time series Dynamic models Physiology Diabetes Glucose Modeling |
| Content Type | Text |
| Resource Type | Article |
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