Sensor Fusion for Glucose Monitoring Systems

Mohamad Al Ahdab, Karim Davari Benam, Hasti Khoshamadi, Anders Lyngvi Fougner, Sebastien Gros

Research output: Contribution to journalConference article in JournalResearchpeer-review

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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 languageEnglish
Book seriesIFAC-PapersOnLine
Issue number2
Pages (from-to)11527-11532
Number of pages6
Publication statusPublished - Nov 2023
Event22nd IFAC World Congress 2023 - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023


Conference22nd IFAC World Congress 2023
Internet address


  • Developments in measurement
  • Diabetes
  • signal processing


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