Noninvasive Blood Glucose Monitoring System Based on Distributed Multi-Sensors Information Fusion of Multi-Wavelength NIR

Bo Zeng,Funing Li,Na Wang,Lintao Hu,Wei Wang,Fulong Zhai

doi:10.4236/eng.2013.510b114

Abstract

In this research, a near infrared multi-wavelength noninvasive blood glucose monitoring system with distributed laser multi-sensors is applied to monitor human blood glucose concentration. In order to improve the monitoring accuracy, a multi-sensors information fusion model based on Back Propagation Artificial Neural Network is proposed. The Root- Mean-Square Error of Prediction for noninvasive blood glucose measurement is 0.088mmol/L, and the correlation coefficient is 0.94. The noninvasive blood glucose monitoring system based on distributed multi-sensors information fusion of multi-wavelength NIR is proved to be of great efficient. And the new proposed idea of measurement based on distri- buted multi-sensors, shows better prediction accuracy.

Highlights

Diabetes has become a modern disease and more than 150 million people are suffering from it all around the world [1]
In order to avoid the weakness of invasive method, a number of noninvasive measurements occurred, like middleinfrared emission spectroscopy [3,4], Near Infrared (NIR) spectroscopy [5,6]
In the noninvasive blood glucose monitoring system based on distributed multi-sensors information fusion of multi-wavelength NIR, this system was designed on the purpose of continuous blood glucose monitoring for patients at home and hospital [2]

Summary

Introduction

Diabetes has become a modern disease and more than 150 million people are suffering from it all around the world [1]. In the noninvasive blood glucose monitoring system based on distributed multi-sensors information fusion of multi-wavelength NIR, this system was designed on the purpose of continuous blood glucose monitoring for patients at home and hospital [2]. Another advantage of this system was the introduction of distributed multi-sensors idea, this change has been proved to be better improvement for blood glucose prediction accuracy. In this distributed multi-sensors system, a multi-sensors information fusion model, Back Propagation Artificial Neural Network (BPANN), was applied

Methods

Results

Conclusion