Analyzing RGB and HSV Color Spaces for Non-Invasive Blood Glucose Level Estimation Using Fingertip Imaging

Traditional blood glucose measurement methods, including finger-prick tests and intravenous sampling, are invasive and can cause discomfort, leading to reduced adherence and stress. Non-invasive BGL estimation addresses these issues effectively. The proposed study focuses on estimating blood glucose levels (BGL) using "Red-Green-Blue (RGB)" and "Hue-Saturation-Value (HSV) color spaces" by analyzing fingertip videos captured with a smartphone camera. The goal is to enhance BGL prediction accuracy through accessible, portable devices, using a novel fingertip video database from 234 subjects. Videos recorded in the "RGB color space" using a smartphone camera were converted into the "HSV color space". The "R channel" from "RGB" and the "Hue channel" from "HSV" were used to generate photoplethysmography (PPG) waves, and additional features like age, gender, and BMI were included to improve predictive accuracy. To enhance the precision of blood glucose estimation, the Genetic Algorithm (GA) was used to identify the most significant and optimal features from the large set of features. The "XGBoost", "CatBoost", "Random Forest Regression (RFR)", and "Gradient Boosting Regression (GBR)" algorithms were applied for blood glucose level (BGL) prediction. Among them, "XGBoost" yielded the best results, with an R-2 value of 0.89 in the "RGB color space" and 0.84 in the "HSV color space", showcasing its superior predictive ability. The experimental outcomes were assessed using "Clarke error grid analysis" and a "Bland-Altman plot". The Bland-Altman analysis showed that only 7.04% of the BGL values fell outside the limits of agreement (+/- 1.96 SD), demonstrating strong agreement with reference values.