Prediabetes detection in unconstrained conditions using wearable sensors

TL;DR

This study investigates using wearable sensors and machine learning to detect prediabetes early, demonstrating promising results with high sensitivity and precision from a small participant sample.

Contribution

It introduces a novel approach combining wearable glucose monitoring and inertial sensors with signal processing and machine learning for prediabetes detection.

Findings

High sensitivity and precision in classification

Feasibility demonstrated with 22 participants

Potential for wearable device-based prediabetes assessment

Abstract

Prediabetes is a common health condition that often goes undetected until it progresses to type 2 diabetes. Early identification of prediabetes is essential for timely intervention and prevention of complications. This research explores the feasibility of using wearable continuous glucose monitoring along with smartwatches with embedded inertial sensors to collect glucose measurements and acceleration signals respectively, for the early detection of prediabetes. We propose a methodology based on signal processing and machine learning techniques. Two feature sets are extracted from the collected signals, based both on a dynamic modeling of the human glucose-homeostasis system and on the Glucose curve, inspired by three major glucose related blood tests. Features are aggregated per individual using bootstrap. Support Vector Machines are used to classify normoglycemic vs. prediabetic individuals. We collected data from 22 participants for evaluation. The results are highly encouraging, demonstrating high sensitivity and precision. This work is a proof of concept, highlighting the potential of wearable devices in prediabetes assessment. Future directions involve expanding the study to a larger, more diverse population and exploring the integration of CGM and smartwatch functionalities into a unified device. Automated eating detecting algorithms can also be used.