학술논문
Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks.
Document Type
Academic Journal
Author
Dénes-Fazakas L; Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.; Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.; Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, 1034 Budapest, Hungary.; Simon B; Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.; Hartvég Á; Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.; Kovács L; Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.; Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.; Dulf ÉH; Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.; Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.; Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Memorandumului Str. 28, 400014 Cluj-Napoca, Romania.; Szilágyi L; Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.; Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.; Computational Intelligence Research Group, Sapientia Hungarian University of Transylvania, 540485 Tîrgu Mureș, Romania.; Eigner G; Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.; Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary.
Source
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
Subject
Language
English
Abstract
Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM.