Faculty Mentor

Dr. Md Shohel Rana

Location

Russell Union Ballroom

Type of Research

Proposed

Session Format

Poster Presentation

College

Allen E. Paulson College of Engineering & Computing

Department

Information Technology

Abstract

Diabetes is a common disease prevalent among a large population of the world, requiring continuous monitoring of blood glucose levels to prevent severe complications of the disease. Currently, continuous glucose monitoring technologies often use invasive and minimally invasive sensors that are not only uncomfortable for the patient but also limit the usability of the device. This project aims to explore cutting-edge technology for continuous glucose monitoring through the fusion of artificial intelligence techniques and novel quantum sensing technologies. A novel framework is proposed for the fusion of machine learning techniques with novel quantum-inspired sensing techniques to improve the accuracy of glucose level estimation. The proposed framework utilizes artificial intelligence techniques to analyze physiological signals to identify small changes or the subtlest patterns associated with glucose level changes. The proposed framework also explores the potential of novel quantum sensing techniques to improve the sensitivity of the signals obtained from the sensors. This research proposes a conceptual framework for a novel intelligent continuous glucose level monitoring system through the fusion of artificial intelligence techniques and novel quantum sensing techniques. The potential of the proposed framework is discussed to revolutionize wearable healthcare technologies. The framework has the potential to provide a safer, more comfortable, and convenient method of managing diabetes complications. The proposed work highlights a potential path for the future of non-invasive medical technologies through the fusion of artificial intelligence techniques and novel quantum sensing technologies.

Program Description

.

DOI

10.20429/GS4.2026.026

Start Date

4-23-2026 2:00 PM

End Date

4-23-2026 4:00 PM

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Apr 23rd, 2:00 PM Apr 23rd, 4:00 PM

AI-Powered Quantum Sensing for Non-Invasive Diabetes Monitoring System

Russell Union Ballroom

Diabetes is a common disease prevalent among a large population of the world, requiring continuous monitoring of blood glucose levels to prevent severe complications of the disease. Currently, continuous glucose monitoring technologies often use invasive and minimally invasive sensors that are not only uncomfortable for the patient but also limit the usability of the device. This project aims to explore cutting-edge technology for continuous glucose monitoring through the fusion of artificial intelligence techniques and novel quantum sensing technologies. A novel framework is proposed for the fusion of machine learning techniques with novel quantum-inspired sensing techniques to improve the accuracy of glucose level estimation. The proposed framework utilizes artificial intelligence techniques to analyze physiological signals to identify small changes or the subtlest patterns associated with glucose level changes. The proposed framework also explores the potential of novel quantum sensing techniques to improve the sensitivity of the signals obtained from the sensors. This research proposes a conceptual framework for a novel intelligent continuous glucose level monitoring system through the fusion of artificial intelligence techniques and novel quantum sensing techniques. The potential of the proposed framework is discussed to revolutionize wearable healthcare technologies. The framework has the potential to provide a safer, more comfortable, and convenient method of managing diabetes complications. The proposed work highlights a potential path for the future of non-invasive medical technologies through the fusion of artificial intelligence techniques and novel quantum sensing technologies.