Harnessing Multimodal Biosensors and Novel Metrics for Predictive Diabetes Management

This INFO 290T project investigates the use of wearable biosensors and machine learning to enhance predictive management of Type 1 diabetes. By collecting continuous biometric data—including pulse rate, pulse rate variability, body temperature, respiratory rate, and electrodermal activity—from an Empatica EmbracePlus, and aligning it with real-time glucose measurements from a Dexcom G7 continuous glucose monitor, the study explores correlations between physiological signals and blood glucose trends.
Advanced modeling techniques revealed a particularly strong predictive relationship between pulse rate variability and glucose levels, suggesting potential for more autonomous, non-invasive glucose monitoring systems with predictive abilities. Additionally, the project introduces the Average Rate of Change (AROC) as a novel metric to complement traditional A1c measurements, offering a dynamic perspective on glycemic variability. These findings advocate for integrating multimodal biosignals into diabetes care to improve real-time insights and support more personalized treatment strategies.