An Accurate Noninvasive Blood Glucose Measurement System Using Portable Near-Infrared Spectrometer and Transfer Learning Framework

Diabetes is considered one of the life-threatening diseases in the world, which needs regular monitoring of blood glucose levels. In this article, we developed a portable system that makes near-infrared spectroscopy (NIRS) technology available to non-professionals through a mobile application and a specially-made enclosure. It overcomes the shortcomings of traditional spectroscopy systems, such as large volume, high cost, complicated operation, and difficulty in online detection. To verify the feasibility of NIRS in noninvasive blood glucose concentration detection, after the pretreatment of the acquired original spectra, we compared two different feature extraction algorithms of synergy interval (Si) and genetic algorithm (GA). On this basis, two quantitative prediction models of partial least squares (PLS) and extreme learning machine (ELM) were established. The experimental results showed the model based on the combination of Si and GA and ELM (i.e., Si-GA-ELM model) as the most accurate among the selected models. At the same time, the prediction accuracy of the spectral waveband was higher than that of the full. To further overcome the difficulty of establishing a finite sample data model and reduce the influence of individual differences, the model transfer method TrAdaBoost was used to enhance the accuracy and stability of our model. The final experimental results show that the NIR spectrometer used is portable and light and can be encased as a handheld device form. Computation models combining machine learning and chemometric methods make the estimated blood glucose more feasible, which is an innovative work in noninvasive blood glucose measurement fields.

Automated summary

Diabetes, a life-threatening disease, requires regular monitoring of blood glucose levels. A portable system using NIRS technology was developed to make noninvasive blood glucose concentration detection feasible for non-professionals. The combination of Si, GA, and ELM models showed the highest prediction accuracy in the experiment.