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Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest.

This publication includes developments of microwave heating achieved inside the H2020 polynSPIRE project. This project has received funding from European Union's Horizon 2020 research and innovation programme under grant agreement No 820665-polynSPIRE project, and reflects the results from microwave heating simulation required inside the Horizon Europe PLASTICE project, focused on technologies of plastic recycling. This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101058540-PLASTICE project.