Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms

Glucose monitoring technologies allow users to monitor glycemic fluctuations (e.g., blood glucose levels). This is particularly important for individuals who have diabetes mellitus (DM). Traditional self-monitoring blood glucose (SMBG) devices require the user to prick their finger and extract a blo...

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Bibliographic Details
Main Authors:	Bogue-Jimenez, B. (Author), Doblas, A. (Author), Huang, X. (Author), Powell, D. (Author)
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	Biometrics Blood Blood glucose Blood glucose level blood glucose self-monitoring Blood glucose self-monitoring Continuous glucose monitoring continuous monitoring Continuous monitoring diabetes mellitus Diabetes mellitus Forecasting glucose Glucose Hyperglycaemia hyperglycemia Learning algorithms machine learning Machine learning Machine learning algorithms Monitoring noninvasive Noninvasive Self-monitoring Wearable sensors
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View Fulltext in Publisher

Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms

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