Non-Invasive Continuous Real-Time Blood Glucose Estimation Using PPG Features-based Convolutional Autoencoder with TinyML Implementation

In this paper, we developed a convolutional autoencoder for non-invasive continuous monitoring of blood glucose levels (BGL) using four photoplethysmography (PPG) features. The model was specifically designed to account for temporal relations among consecutive PPG segments' features and transie...

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Bibliographic Details

Published in:	IEEE International Symposium on Circuits and Systems proceedings pp. 1 - 5
Main Authors:	Ali, Noor Faris, Aldhaheri, Alyazia, Wodajo, Bethel, Alshamsi, Meera, Alshamsi, Shaikha, Atef, Mohamed
Format:	Conference Proceeding
Language:	English
Published:	IEEE 19.05.2024
Subjects:	Blood Glucose Convolutional Autoencoder Glucose Integrated circuit modeling Machine Learning MCU PPG Predictive models Real-time systems Stability analysis TinyML Topology Transient analysis
ISSN:	2158-1525
Online Access:	Get full text
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