Unobtrusive Continuous Multi-Metabolite Monitoring for a Physiological Care of Insulin-treated Diabeteslearn more


  1. Bring disruptive change to diabetes care with the potential to spill-over into other applications. The development of an implantable device with 8 years battery lifetime and one year insulin refill cycle will challenge state-of-the-art diabetes management systems, and offer convenient solutions applicable to other metabolites, medical conditions and dosing supply systems. The project aims to disrupt current diabetes treatment therapy by combining substance levels monitoring and prompt insulin delivery in a single device 8 years battery lifetime and one year insulin refill cycle, thus bringing diabetes care from an obtrusive, episodic process to an unobtrusive, continuous way.

  2. Development of a long-term calibration-free MM sensor. The developed device will be designed to measure simultaneously 3 substances instead of solely relying on glucose levels monitoring. By also measuring concentration in lactate (marker for an- aerobic or intense muscle activity) and ketone bodies (secondary fuel source of the body), the project will offer a clearer view of metabolic health. A prediction algorithm model will be able to forecast accurately the concentration of glucose, lactate and ketones.

  3. Creation of a fully implantable physiologic high-precision insulin delivery system connected to the multi-sensing device. The project will improve the current state of the art in high-precision pumping technology: the MEMS diaphragm pump. By integrating convenient features enabling highest precision in system disturbances detection, MuSiC4Diabetes will introduce new cutting-edge technology for efficient diabetes management.

  4. MM data science. Temporal relationships between MM signal significantly reduce the system delays allowing a prompt control action. New input-output models of the relationship between the three key physiological signals (glucose, lactate, ketone bodies) and advanced adaptive personalized multi-target control techniques will be developed. To optimise a prompt control action of the device, new data models analysing the relationships between the three key substances (glucose, lactate, ketone bodies) and monitoring techniques will be developed. By choosing the Model Predictive Control (MPC), the project will support optimal insulin delivery.

  5. From obtrusive to unobtrusive ITD management: human factors. To evaluate the relevance of the solutions created in MuSiC4Diabetes, the project will gather input from patients living with diabetes. Their testimonies will better define what easy diabetes management means to them, figure out the pros and cons of multi-metabolite sensing, and find areas where the project can reduce diabetes-related stress. An assessment tool will ease the evaluation process, while an online education and training program will offer insights on the system.

  6. Clinical/animal validation of system components and in silico system validation. The feasibility and safety of implantation of the MM monitoring sensor will be assessed with insulin-treated diabetes patient volunteers in a controlled environment, and will also reveal the accuracy of signal detection. IP insulin delivery will be further tested in proof-of-concept.