IEEE - Institute of Electrical and Electronics Engineers, Inc. - Data-Driven Anomaly Recognition for Unsupervised Model-Free Fault Detection in Artificial Pancreas

Author(s): Lorenzo Meneghetti ; Matteo Terzi ; Simone Del Favero ; Gian Antonio Susto ; Claudio Cobelli
Sponsor(s): IEEE Control Systems Society
Publisher: IEEE - Institute of Electrical and Electronics Engineers, Inc.
Volume: PP
Page(s): 1 - 15
ISSN (CD): 2374-0159
ISSN (Electronic): 1558-0865
ISSN (Paper): 1063-6536
DOI: 10.1109/TCST.2018.2885963
Regular:

The last decade has seen tremendous improvements in technologies for Type 1 Diabetes (T1D) management, in particular the so-called artificial pancreas (AP), a wearable closed-loop device... View More

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