Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML

Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML

Abstract Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-invasive alternative for estimating blood g...

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Bibliographic Details
Main Authors: Mahdi Zeynali, Khalil Alipour, Bahram Tarvirdizadeh, Mohammad Ghamari
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
photoplethysmography
blood glucose level estimation
deep learning
ResNet
TinyML
Online Access:https://doi.org/10.1038/s41598-024-84265-8
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https://doi.org/10.1038/s41598-024-84265-8

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