Metaverse-edge cloud assisted glucose monitoring with nutrition and activities in interoperate transfer learning

Mastoi, Qurat-Ul-Ain; Lakhan, Abdullah; Syed, Toqeer Ali; Albouq, Sami Saad

Abstract

Nowadays, the prevalence of type 2 diabetes in humans is steadily increasing due to dietary habits and sedentary lifestyles. As a result, high-carbohydrate diets have caused imbalanced glucose levels, affecting people with diabetes since medieval times. To address this problem, our paper introduces the innovative concept of MEGMNT-ITL (Metaverse-edge glucose monitoring with nutrition and activities in interoperable transfer learning). We present a novel and pioneering approach that combines metaverse technology with real-time glucose monitoring using smartwatches. Our objective is to reduce the chances of developing type 2 diabetes based on a healthy diet and the effects of the subject in practice. Due to resource constraints in smartwatches, we offload workloads to remote edge cloud servers to alleviate the computational issues of local systems by integrating transfer learning methodologies. The adaptive offloading scheme is suggested to allow for more efficient and precise glucose monitoring, further enhanced by considering variables such as nutrition and physical activity. Transfer learning ensures that monitoring algorithms adapt to individual user behaviors and environmental conditions, enhancing detection accuracy. Overall, this methodology signifies a significant breakthrough in healthcare technology, with potential benefits for individuals managing diabetes and related conditions.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Mobile Web and Intelligent Information Systems
• Start Date: Aug 11, 2025
• Acceptance Date: Apr 24, 2025
• Online Publication Date: Aug 11, 2025
• Publication Date: Aug 11, 2025
• Deposit Date: Aug 28, 2025
• Publicly Available Date: Aug 12, 2026
• Peer Reviewed: Peer Reviewed
• Volume: 16066
• Pages: 287–301
• Series Title: Lecture Notes in Computer Science
• DOI: https://doi.org/10.1007/978-3-032-02060-4_20
• Public URL: https://uwe-repository.worktribe.com/output/14920808