A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems
The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant ope...
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MDPI AG
2021-10-01
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Series: | Sensors |
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Online Access: | https://www.mdpi.com/1424-8220/21/21/7117 |
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author | Aleix Beneyto Vicenç Puig B. Wayne Bequette Josep Vehi |
author_facet | Aleix Beneyto Vicenç Puig B. Wayne Bequette Josep Vehi |
author_sort | Aleix Beneyto |
collection | DOAJ |
description | The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems. |
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format | Article |
id | doaj.art-72f58f5d88a944ca8eaa771f1af2db1f |
institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-03-10T05:52:56Z |
publishDate | 2021-10-01 |
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spelling | doaj.art-72f58f5d88a944ca8eaa771f1af2db1f2023-11-22T21:36:52ZengMDPI AGSensors1424-82202021-10-012121711710.3390/s21217117A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas SystemsAleix Beneyto0Vicenç Puig1B. Wayne Bequette2Josep Vehi3Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, SpainAutomatic Control Department-Campus de Terrassa, Universitat Politècnica de Catalunya (UPC), 08222 Terrassa, SpainDepartment of Chemical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USADepartment of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, SpainThe use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.https://www.mdpi.com/1424-8220/21/21/7117artificial pancreashybrid automatonKalman filterpatient in the looptype 1 diabetes |
spellingShingle | Aleix Beneyto Vicenç Puig B. Wayne Bequette Josep Vehi A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems Sensors artificial pancreas hybrid automaton Kalman filter patient in the loop type 1 diabetes |
title | A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems |
title_full | A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems |
title_fullStr | A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems |
title_full_unstemmed | A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems |
title_short | A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems |
title_sort | hybrid automata approach for monitoring the patient in the loop in artificial pancreas systems |
topic | artificial pancreas hybrid automaton Kalman filter patient in the loop type 1 diabetes |
url | https://www.mdpi.com/1424-8220/21/21/7117 |
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