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Abstract
A fully automated artificial pancreas (AP) requires accurate blood glucose (BG) readings. However, many factors can affect the accuracy of commercially available sensors. These factors include sensor artifacts due to the pressure on surrounding tissues, connection loss, and poor calibration. The AP may administer an incorrect insulin bolus due to inaccurate sensor data when the patient is not supervising the system. The situation can be even worse in animal experiments because animals are eager to play with the sensor and apply pressure. In this study, we propose and derive a Multi-Model Kalman Filter with Forgetting Factor (MMKFF) for the problem of fusing information from redundant subcutaneous glucose sensors. The performance of the developed MMKFF was assessed by comparing it against other Kalman Filter (KF) strategies on experimental data obtained in two different animals. The developed MMKFF was shown to provide a reliable fused glucose reading. Additionally, compared to the other KF approaches, the MMKFF was shown to be better able to adjust to changes in the accuracy of the glucose sensors.
Original language | English |
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Book series | IFAC-PapersOnLine |
Volume | 56 |
Issue number | 2 |
Pages (from-to) | 11527-11532 |
Number of pages | 6 |
ISSN | 1474-6670 |
DOIs | |
Publication status | Published - Nov 2023 |
Event | 22nd IFAC World Congress 2023 - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 https://www.ifac2023.org/ |
Conference
Conference | 22nd IFAC World Congress 2023 |
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Country/Territory | Japan |
City | Yokohama |
Period | 09/07/2023 → 14/07/2023 |
Internet address |
Keywords
- Developments in measurement
- Diabetes
- signal processing
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Dive into the research topics of 'Sensor Fusion for Glucose Monitoring Systems'. Together they form a unique fingerprint.Projects
- 1 Active
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ADAPT-T2D: Adherence through cloud-based Personalised Treatment for Type 2 Diabetes
Jensen, M. H., Stoustrup, J., Hangaard, S., Larsen, T. K. & Thomsen, C. H. N.
01/12/2019 → 30/10/2025
Project: Research