QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning

<p dir="ltr">Patients with hyperglycemia require routine glucose monitoring to effectively treat their condition. We have developed a lightweight wristband device to capture Photoplethysmography (PPG) signals. We collected PPG signals, demographic information, and blood pressure data...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Md Nazmul Islam Shuzan (21842426) (author)
مؤلفون آخرون:	Moajjem Hossain Chowdhury (21842429) (author), Muhammad E. H. Chowdhury (14150526) (author), Khalid Abualsaud (16888701) (author), Elias Yaacoub (14150586) (author), Md Ahasan Atick Faisal (21842432) (author), Mazun Alshahwani (21842435) (author), Noora Al Bordeni (21842438) (author), Fatima Al-Kaabi (21842441) (author), Sara Al-Mohannadi (21842444) (author), Sakib Mahmud (15302404) (author), Nizar Zorba (16888728) (author)
منشور في:	2024
الموضوعات:	Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning Continuous glucose monitoring (CGM) Internet of Things (IoT) machine learning photoplethysmography (PPG) wearable device Glucose Diabetes Blood Biomedical monitoring Monitoring Skin Insulin Internet of Things Photoplethysmography
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QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning

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