Kuang, L;
Zhu, T;
Li, K;
Daniels, J;
Herrero, P;
Georgiou, P;
(2021)
Live Demonstration: An IoT Wearable Device for Real-time Blood Glucose Prediction with Edge AI.
In:
BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings.
IEEE: Berlin, Germany.
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Abstract
Blood glucose (BG) prediction is crucial to the successful management of type 1 diabetes (T1D) by allowing for proactive medical interventions and treatment. We present an IoT-enabled wearable device for real-time BG prediction based on continuous glucose monitoring (CGM) and a novel attention-based recurrent neural network (RNN). The complete hardware contains a system on a chip (SoC) that enables BLE connectivity and executes the embedded RNN with edge inference. This device can provide 24-hour predictive glucose alerts, i.e., hypoglycemia, to improve BG control and prevent or mitigate potential complications. Meanwhile, it can be connected to desktop computers and smartphones for the visualization of BG trajectories, data storage, and model update.
Type: | Proceedings paper |
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Title: | Live Demonstration: An IoT Wearable Device for Real-time Blood Glucose Prediction with Edge AI |
Event: | 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS) |
Location: | ELECTR NETWORK |
Dates: | 6 Oct 2021 - 9 Oct 2021 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/BioCAS49922.2021.9644975 |
Publisher version: | http://doi.org/10.1109/BioCAS49922.2021.9644975 |
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: | Science & Technology, Technology, Computer Science, Information Systems, Computer Science, Interdisciplinary Applications, Engineering, Biomedical, Engineering, Electrical & Electronic, Computer Science, Engineering |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
URI: | https://discovery.ucl.ac.uk/id/eprint/10163914 |
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