Enhancing the Accuracy of Non-Invasive Glucose Sensing in Aqueous Solutions Using Combined Millimeter Wave and Near Infrared Transmission

We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37-39 GHz and 900-1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.

Automated summary

The study reported measurement results of non-invasive glucose sensing using a novel multiwavelength approach that combined radio frequency and near infrared signals. Changes in glucose solutions with varying concentrations were successfully detected, and it was shown for the first time that combining transmission data from distinct electromagnetic bands, accompanied by machine learning algorithms, can achieve greater accuracy in predicting glucose levels compared to single modality systems.