Constrained IoT-based machine learning for accurate glycemia forecasting in type 1 diabetes patients
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Rodríguez Rodríguez, Ignacio; Campo Valera, María Mercedes; Rodríguez Rodríguez, José Víctor; Frisa Rubio, AlbertoÁrea de conocimiento
Teoría de la Señal y las ComunicacionesPatrocinadores
Ignacio Rodríguez-Rodríguez would like to thank Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI), Junta de Andalucía, Spain. María Campo-Valera is grateful for postdoctoral program Margarita Salas—Spanish Ministry of Universities (financed by European Union—NextGenerationEU)Fecha de publicación
2023Editorial
MDPICita bibliográfica
Rodríguez-Rodríguez, I.; Campo-Valera, M.; Rodríguez, J.-V.; Frisa-Rubio, A. Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients. Sensors 2023, 23, 3665. https://doi.org/10.3390/s23073665Palabras clave
Constrained devicesDiabetes
IoT
Monitoring
Machine learning
Resumen
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 ...
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