Zhu, T;
Kuang, L;
Daniels, J;
Herrero, P;
Li, K;
Georgiou, P;
(2022)
IoMT-Enabled Real-time Blood Glucose Prediction with Deep Learning and Edge Computing.
IEEE Internet of Things Journal
10.1109/JIOT.2022.3143375.
(In press).
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Abstract
Blood glucose (BG) prediction is essential to the success of glycemic control in type 1 diabetes (T1D) management. Empowered by the recent development of the Internet of Medical Things (IoMT), continuous glucose monitoring (CGM) and deep learning technologies have been demonstrated to achieve the state of the art in BG prediction. However, it is challenging to implement such algorithms in actual clinical settings to provide persistent decision support due to the high demand for computational resources, while smartphone-based implementations are limited by short battery life and require users to carry the device. In this work, we propose a new deep learning model using an attention-based evidential recurrent neural network and design an IoMT-enabled wearable device to implement the embedded model, which comprises a low-cost and low-power system on a chip to perform Bluetooth connectivity and edge computing for real-time BG prediction and predictive hypoglycemia detection. In addition, we developed a smartphone app to visualize BG trajectories and predictions, and desktop and cloud platforms to backup data and fine-tune models. The embedded model was evaluated on three clinical datasets including 47 T1D subjects. The proposed model achieved superior performance of root mean square error (RMSE), mean absolute error, and glucose-specific RMSE, and obtained the best accuracy for hypoglycemia detection when compared with a group of machine learning baseline methods. Moreover, we performed hardware-in-the-loop in silico trials with 10 virtual T1D adults to test the whole IoMT system with predictive low-glucose management, which significantly reduced hypoglycemia and improved BG control.
| Type: | Article |
|---|---|
| Title: | IoMT-Enabled Real-time Blood Glucose Prediction with Deep Learning and Edge Computing |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JIOT.2022.3143375 |
| Publisher version: | https://doi.org/10.1109/JIOT.2022.3143375 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Diabetes, deep learning, Internet of Things (IoT), edge computing, glucose prediction, artificial intelligence |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10143535 |
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