VELAZQUEZ project is a ground-breaking proposal aimed at developing sophisticated personalized, gender-aware, non-invasive and selective blood glucose sensors for the benefit of people with diabetes. Diabetes is a metabolic disease characterized by blood glucose levels out of the healthy range during prolonged times, chiefly due to insulin disfunction or unavailability. Diabetes and its associated complications constitute a remarkable health and economic problem, which nowadays shows a worrying growing trend. With no known cure, diabetes treatment depends on a strict monitoring of the blood glucose level followed by corrective actions through insulin injections or glucose intake. Nowadays the most common glucose monitoring methods are invasive, uncomfortable, painful, and with an intrinsic intermittent nature. Furthermore, these methods are gender-blind, whereas the medical evidence points that some unique features of female gender, such as the menstruation cycle, have an impact on the blood glucose level and therefore on the management of diabetes in women. Due to all these reasons, research on gender-aware, personalized, non-invasive and continuous blood glucose sensors is of paramount importance for bettering diabetes management.

Although some attempts have been made considering different techniques, the technology attracting the greatest attention in this regard is microwave sensors due to their unique advantages, such as non-enzymatic nature, continuous operation, non-invasive capabilities, cost-effectiveness, robustness and ease of integration. The working principle is simple but effective: a variation in the glucose concentration of a solution (such as blood) yields a variation in the complex dielectric permittivity of the medium, which can be effectively tracked through the associated changes in response of a sensor made with microwave resonator techniques. These sensors are indeed able to operate in non-invasive mode. The last advancements with these techniques allowed for the detection of medically relevant glucose concentrations with microwave resonant sensors. No longer limited by the sensitivity, their real application now faces a new challenge: the selectivity.

In addition to the hardware developments the recent years have been doubtlessly marked by the development of artificial intelligence (AI) techniques, mostly machine learning algorithms, in almost all the scientific fields. Particularly, the use of AI for performance boosting of microwave glucose sensors has shown initial good results, with high resolutions and considerably low errors. Not only that, but the preliminary trials suggest that, fed with individual physiological information (gender, age, menstruation cycle day), these AI techniques could indeed notably improve the selectivity of these non-invasive sensors, providing for the craved personalized, gender-aware blood glucose measurement. Like a painting by Diego Velázquez, this project brings a new perspective to microwave glucose sensing by involving all the visible (significant) features with a combination of direct and simple, yet effective, techniques such as state-of-the-art selective microwave sensors and AI algorithms. An impressionistic glucose measurement.