About this document
0A Noninvasive Blood Glucose Monitoring System Based On Smartphone PPG Signal Processing and Machine Learning by Sinan Demir is a document available to read on EtoBox.
This document presents a noninvasive blood glucose monitoring system that utilizes smartphone photoplethysmography (PPG) signals and machine learning algorithms to estimate blood glucose levels. The system demonstrated a high accuracy of 97.54% in distinguishing valid signals and an overall accuracy of 81.49% in estimating glucose levels from a dataset of 80 subjects. This approach aims to provide a portable and user-friendly alternative to invasive glucose measurement techniques for daily health monitoring
- Author
- Sinan Demir
- Language
- EN