Comparison of feature learning methods for non-invasive interstitial glucose prediction using wearable sensors in healthy cohorts: a pilot study

• Published in: Intelligent medicine Vol. 4; no. 4; pp. 226 - 238
• Main Authors: Huang, Xinyu, Schmelter, Franziska, Uhlig, Annemarie, Irshad, Muhammad Tausif, Nisar, Muhammad Adeel, Piet, Artur, Jablonski, Lennart, Witt, Oliver, Schröder, Torsten, Sina, Christian, Grzegorzek, Marcin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2024; Institute of Medical Informatics, University of Lübeck, Germany%Institute of Nutritional Medicine, University of Luebeck and University Medical Center Schleswig-Holstein, Lübeck, Germany%Department of IT, University of the Punjab, Lahore, Pakistan%Perfood GmbH, Research & Development, Lübeck, Germany%Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE), Lübeck, Germany%German Research Center for Artificial Intelligence (DFKI), Lübeck, Germany
• Subjects: Clarke error grid; Deep learning; Interstitial glucose prediction; Non-invasive glucose monitoring; Physiological signal processing; Wearable sensors; Deep learning; Clarke error grid; Physiological signal processing; Interstitial glucose prediction; Wearable sensors; Non-invasive glucose monitoring

Alterations in glucose metabolism, especially the postprandial glucose response (PPGR), are crucial contributors to metabolic dysfunction, which underlies the pathogenesis of metabolic syndrome. Personalized low-glycemic diets have shown promise in reducing postprandial glucose spikes. However, curr...