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

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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Bibliographic Details
Main Author: Md Nazmul Islam Shuzan (21842426) (author)
Other Authors: 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)
Published: 2024
Subjects:
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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