@inproceedings{e31675603f5c4d2984aca1e3617a5121,
title = "Optimal blood glucose prediction based on intermittent data from wearable glucose monitoring sensors",
abstract = "Blood glucose prediction is to predict the glucose trend over time based on historical glucose data, and it plays a crucial role in the closed-loop control of artificial pancreas, which can reduce the risk of complications by regulating insulin dose and injection time. This paper proposes a Kalman-filter-based glucose prediction method through minimizing the mean square prediction error, which assumes that the data is sampled every 15 min from a wearable flash glucose monitoring sensor. This method calculates glucose estimates every 5 min and provides glucose predictions for the next 30 min. The method is evaluated on in-silico data generated from the 10-adult cohort of the US FDA-accepted UVA/Padova T1DM simulator. The predicted results are compared with CGM data with 5-min sample-period through multiple metrics, including the mean square prediction error and the mean absolute relative deviation. The results show that the performance of the proposed approach with slow-rate glucose data (15 min) is close to that obtained based on fast-rate data (5 min).",
keywords = "Blood Glucose Prediction, Kalman Filtering, Mean Square Prediction Error, Sampled Data, Wearable Devices",
author = "Lijun Hou and Huipeng Zhang and Junzheng Wang and Dawei Shi",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8866572",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "5463--5467",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
address = "United States",
}