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AuthorAlhaddad, Ahmad Yaser
AuthorAly, Hussein
AuthorGad, Hoda
AuthorAl-Ali, Abdulaziz
AuthorSadasivuni, Kishor Kumar
AuthorCabibihan, John John
AuthorMalik, Rayaz A.
Available date2023-05-17T10:24:05Z
Publication Date2022-05-12
Publication NameFrontiers in Bioengineering and Biotechnology
Identifierhttp://dx.doi.org/10.3389/fbioe.2022.876672
CitationAlhaddad, A. Y., Aly, H., Gad, H., Al-Ali, A., Sadasivuni, K. K., Cabibihan, J. J., & Malik, R. A. (2022). Sense and learn: recent advances in wearable sensing and machine learning for blood glucose monitoring and trend-detection. Frontiers in Bioengineering and Biotechnology, 10, 699.
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85131197443&origin=inward
URIhttp://hdl.handle.net/10576/42853
Abstract(Figure presented.) Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.
SponsorThe work is supported by an NPRP grant from the Qatar National Research Fund under the grant No. NPRP 11S-0110-180247.
Languageen
PublisherFrontiers Media S.A.
Subjectblood glucose management
bodily fluids glucose
deep learning
diabetes mellitus
hypoglycemia
machine learning
non-invasive wearables and sensors
TitleSense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection
TypeArticle
Volume Number10
ESSN2296-4185
dc.accessType Open Access


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