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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE International Symposium on Circuits and Systems proceedings S. 1 - 5
Hauptverfasser:	Ali, Noor Faris, Aldhaheri, Alyazia, Wodajo, Bethel, Alshamsi, Meera, Alshamsi, Shaikha, Atef, Mohamed
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 19.05.2024
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!