Proactive Diabetic Health Monitoring: An Internet of Things–Driven Framework Utilizing a Correlation Attention-Based Dense BiRNN Model

This project builds a practical framework for proactive diabetes monitoring by combining IoT biosensors with modern deep‑learning models. Conventional diabetes care usually depends on occasional clinic visits, which do not give the continuous, real‑time feedback needed for tight blood‑sugar control. Our system solves this by using wearable biosensors that constantly record physiological signals and send the data securely to a central monitoring platform. The heart of the analysis is a Correlation‑Attention‑based Dense Bidirectional Recurrent Neural Network (CA‑DBiRNN). This network processes the time‑ordered health measurements and, thanks to its correlation‑attention module, spots important temporal links and dependencies across the multiple sensor streams. As a result, it can flag abnormal glucose patterns

early and trigger fast clinical action. The overall design shifts diabetes management from a reactive, visit‑driven approach to a proactive, self‑managed one. Engineering students can use this architecture to explore how edge‑level data collection works together with cloud‑based deep‑learning inference, providing a solid base for research and implementation of real‑time health‑monitoring systems.

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