The development of fully autonomous artificial pancreas systems (APS) that independently regulate the glucose levels of patients with Type 1 diabetes has been a long-standing goal of diabetes research. A significant barrier to progress is the difficulty of testing new control algorithms and safety features, since clinical trials are time- and resource-intensive. To facilitate ease of validation, we propose an open-source APS testbed that can integrate state-of-the-art APS controllers and glucose simulators with a novel fault injection engine. The testbed is used to reproduce the blood glucose trajectories of real patients from a clinical trial conducted over six months. We evaluate the performance of two closed-loop control algorithms (OpenAPS and Basal Bolus) using the testbed and find that these control algorithms are able to keep blood glucose in a safe region 93.49% and 79.46% of the time on average, compared with 66.18% of the time for the clinical trial. The fault injection engine simulates the real recalls and adverse events reported to the U.S. Food and Drug Administration (FDA) and demonstrates the resilience of the controller in hazardous conditions. We use the testbed to generate 2.5 years of synthetic data representing 20 different patient profiles with realistic adverse event scenarios, which would have been expensive and risky to collect in a clinical trial.
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Modeling Climate Management in a Smart Home using a Scaled Testbed with Accelerated Time
A smart home with a controller that can understandand predict the interaction between the external environment and the user’s behavior and preferences can provide significant energy
efficiency and savings. Unfortunately, experimentation of real world homes for the development of such a controller is prohibitively expensive. In this paper we describe techniques through which such experiments can be performed on scaled testbed with an
accelerated time. We illustrate how the modeling of different geographical areas can be performed by the mapping of the model’s temperature and time to their real-world equivalents. We train three different machine learning models for predicting different sensor readings in the testbed, and find that the achieved predictive accuracy supports the feasibility of the development of future smart climate controllers.
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- Award ID(s):
- 1852002
- NSF-PAR ID:
- 10410227
- Date Published:
- Journal Name:
- IEEE International Conference on Computer Communications (ICC)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICC45855.2022.9882274
