Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning

TL;DR

This paper presents a novel deep learning approach using phasic image representation and recurrence plots to enhance real-time glucose monitoring from wearables, especially effective with limited data, surpassing current accuracy benchmarks.

Contribution

Introduces a new machine learning method combining frequency domain recurrence plots with deep learning for improved glucose prediction from wearable data with small datasets.

Findings

Achieved over 87% accuracy in real-time glucose prediction.

Outperformed existing models on historical datasets.

Demonstrated effectiveness with limited data samples.

Abstract

In the U.S., over a third of adults are pre-diabetic, with 80\% unaware of their status. This underlines the need for better glucose monitoring to prevent type 2 diabetes and related heart diseases. Existing wearable glucose monitors are limited by the lack of models trained on small datasets, as collecting extensive glucose data is often costly and impractical. Our study introduces a novel machine learning method using modified recurrence plots in the frequency domain to improve glucose level prediction accuracy from wearable device data, even with limited datasets. This technique combines advanced signal processing with machine learning to extract more meaningful features. We tested our method against existing models using historical data, showing that our approach surpasses the current 87\% accuracy benchmark in predicting real-time interstitial glucose levels.