On the possibility of predicting glycaemia 'on the fly' with constrained IoT devices in type 1 diabetes mellitus patients
Autor
Rodríguez Rodríguez, Ignacio; Rodríguez Rodríguez, José Víctor; Chatzigiannakis, Ioannis; Zamora Izquierdo, Miguel ÁngelÁrea de conocimiento
Ingeniería Eléctrica; Ingeniería Química; Química-FísicaPatrocinadores
The authors would like to thank to the Endocrinology Department of the Morales Meseguer and Virgen de la Arrixaca hospitals of the city of Murcia (Spain). This work has been sponsored by the Spanish Ministry of Economy and Competitiveness through 387 the PERSEIDES (ref. TIN2017-86885-R) and CHIST-ERA (ref. PCIN-2016-010) projects; by MINECO grant BES-2015-071956 and by the European Comission through the H2020-ENTROPY-649849 EU ProjectRealizado en/con
Universidad Politécnica de Cartagena; Universidad de MurciaFecha de publicación
2019-10-18Editorial
MDPICita bibliográfica
Rodríguez-Rodríguez I, Rodríguez J-V, Chatzigiannakis I, Zamora Izquierdo MÁ. On the Possibility of Predicting Glycaemia ‘On the Fly’ with Constrained IoT Devices in Type 1 Diabetes Mellitus Patients. Sensors. 2019; 19(20):4538. https://doi.org/10.3390/s19204538Revisión por pares
SiPalabras clave
Continuous glucose monitoringWearable devices
Constrained devices
Time series forecasting
Machine learning
Resumen
Type 1 Diabetes Mellitus (DM1) patients are used to checking their blood glucose levels several times per day through finger sticks and, by subjectively handling this information, to try to predict their future glycaemia in order to choose a proper strategy to keep their glucose levels under control, in terms of insulin dosages and other factors. However, recent Internet of Things (IoT) devices and novel biosensors have allowed the continuous collection of the value of the glucose level by means of Continuous Glucose Monitoring (CGM) so that, with the proper Machine Learning (ML) algorithms, glucose evolution can be modeled, thus permitting a forecast of this variable. On the other hand, glycaemia dynamics require that such a model be user-centric and should be recalculated continuously in order to reflect the exact status of the patient, i.e., an ‘on-the-fly’ approach. In order to avoid, for example, the risk of being disconnected from the Internet, it would be ideal if this task could ...
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