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dc.contributor.authorAl Ahdab, Mohamad
dc.contributor.authorDavari Benam, Karim
dc.contributor.authorKhoshamadi, Hasti
dc.contributor.authorFougner, Anders Lyngvi
dc.contributor.authorGros, Sebastien Nicolas
dc.date.accessioned2023-11-24T13:34:07Z
dc.date.available2023-11-24T13:34:07Z
dc.date.created2023-11-07T12:02:54Z
dc.date.issued2023
dc.identifier.citationIFAC-PapersOnLine. 2023, 56 (2), 11527-11532.en_US
dc.identifier.issn2405-8963
dc.identifier.urihttps://hdl.handle.net/11250/3104579
dc.description.abstractA 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.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S240589632300811X
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectDiabetesen_US
dc.subjectDiabetesen_US
dc.subjectKontinuerlig glukosemålingen_US
dc.subjectContinuous glucose measurementen_US
dc.subjectKalman filteren_US
dc.subjectKalman filteren_US
dc.subjectsensorfusjonen_US
dc.subjectSensor fusionen_US
dc.titleSensor Fusion for Glucose Monitoring Systemsen_US
dc.title.alternativeSensor Fusion for Glucose Monitoring Systemsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.subject.nsiVDP::Medisinsk teknologi: 620en_US
dc.subject.nsiVDP::Medical technology: 620en_US
dc.source.pagenumber11527-11532en_US
dc.source.volume56en_US
dc.source.journalIFAC-PapersOnLineen_US
dc.source.issue2en_US
dc.identifier.doi10.1016/j.ifacol.2023.10.444
dc.identifier.cristin2193202
dc.relation.projectNorges forskningsråd: 248872en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal