Improvement of Non-invasive Glucose Estimation Accuracy through Multi-wavelength PPG.

Abstract

Effective diabetes management requires regular and accurate blood glucose monitoring; however, traditional invasive methods often cause discomfort and inconvenience. Non-invasive techniques such as photoplethysmography (PPG) have been explored, though single-wavelength PPG systems are limited by the overlapping absorption characteristics between glucose and other biological components, such as water and fat. In this study, a novel multi-wavelength PPG system integrated with temperature and humidity sensors is introduced, coupled with a neural network framework featuring attention mechanisms to enhance glucose prediction. The system employs six optical sensors covering wavelengths from the visible to near-infrared (NIR) spectrum, enabling deeper tissue penetration and enhanced glucose specificity by targeting distinct absorption peaks-especially those above 1000 nm. The system was validated using a robust dataset of 26,063 measurements from 254 participants. The experimental results demonstrate significant improvements, with the model achieving 86.49% compliance with the ISO 15197: 2013 standards and 91.80% of measurements falling within Zone A of the Parkes error grid. The introduction of multiple wavelengths clearly improves performance over single-wavelength systems, and wavelengths above 1000 nm were shown to have a higher contribution in glucose prediction. In addition, the incorporation of temperature and humidity data also enhanced performance by accounting for environmental and physiological factors, and that demographic and meal-related factors significantly impact prediction accuracy, thereby underscoring the potential of this system as a reliable, non-invasive, and personalized glucose monitoring tool.