537 million people live with diabetes worldwide, and specialists predict a 48% increase of this population by 2045. Diabetes management often requires the monitoring of blood glucose, a process which traditionally involves pricking your fingertip with a lancet several times a day to evaluate the need for insulin shots. Such a complicated (and painful!) process makes it hard for people to accept their diabetes diagnostic and treat the condition adequately – until recent technological development revolutionised the way we monitor and treat diabetes!

The invention and popularisation of continuous glucose monitors (CGM) make it possible for people to monitor their glucose levels 24/7 without picking fingers; injection pens containing insulin and GLP-1 hormones make it easier to apply the medication without vials and long needles. Whilst these solutions bring a better life quality for people living diabetes, they also bring a new challenge: what to do with all this newly generated waste? The answer might come quickly to one’s mind: Easy – let’s RECYCLE IT! But how can we guarantee that high quality materials are obtained at the end of this recycling operation?

That is the challenge we aim to overcome with R(E)YE, a machine-learning driven sensing system for recycling of diabetes-related waste. We will rely on sensors operating at fast speeds, and on a machine-learning algorithm trained to identify the main devices used for diabetes management, along with their composition. By knowing the exact materials present in each item, we enable an efficient recycling and reduction of the carbon footprint associated with management of diabetes.