Research Article
Pervasive Glucose Monitoring: A Non-invasive Approach Based on Near-Infrared Spectroscopy
@INPROCEEDINGS{10.1007/978-3-031-59717-6_19, author={Maria Valero and Katherine Ingram and Anh Duong and Valentina Nino}, title={Pervasive Glucose Monitoring: A Non-invasive Approach Based on Near-Infrared Spectroscopy}, proceedings={Pervasive Computing Technologies for Healthcare. 17th EAI International Conference, PervasiveHealth 2023, Malm\o{}, Sweden, November 27-29, 2023, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2024}, month={6}, keywords={Glucose Monitoring Non-invasive Spectroscopy Diabetes Sensors Machine Learning}, doi={10.1007/978-3-031-59717-6_19} }- Maria Valero
Katherine Ingram
Anh Duong
Valentina Nino
Year: 2024
Pervasive Glucose Monitoring: A Non-invasive Approach Based on Near-Infrared Spectroscopy
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-031-59717-6_19
Abstract
With more than 12% of Americans living with diabetes and more than 30% suffering from metabolic syndrome, the United States is facing the need for more technology for easy and non-invasive blood glucose monitoring. The current pervasive technologies can be leveraged as the foundation for new sensor devices and intelligent models to monitor and manage glucose. This paper presents an approach for monitoring glucose concentration with a pervasive device. The device capture and processes spectroscopy images of a body’s extremity using a powerful machine learning model. The spectroscopy or spectral image is based on the theory of light intensity data from the spectrum. Using light absorption, the proposed sensor executes a model that permits glucose estimation. The procedure is noninvasive as no blood or needles are required. The device also pairs the information to a mobile application for real-time monitoring. Preliminary studies show an accuracy of 90.78% compared with traditional blood glucose estimation.
