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

• 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

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...