Towards Affordable, Non-Invasive Real-Time Hypoglycemia Detection Using Wearable Sensor Signals

TL;DR

This study presents a multimodal, non-invasive wearable sensor framework for real-time hypoglycemia detection, combining physiological signals and advanced machine learning models to improve sensitivity and accessibility in diabetes management.

Contribution

It introduces a comprehensive multimodal approach using GSR and HR signals with deep learning models, demonstrating improved detection performance over single-signal methods.

Findings

Multimodal fusion enhances detection sensitivity.

Deep temporal models outperform traditional classifiers.

Physiological signals show distinct patterns before hypoglycemia.

Abstract

Accurately detecting hypoglycemia without invasive glucose sensors remains a critical challenge in diabetes management, particularly in regions where continuous glucose monitoring (CGM) is prohibitively expensive or clinically inaccessible. This extended study introduces a comprehensive, multimodal physiological framework for non-invasive hypoglycemia detection using wearable sensor signals. Unlike prior work limited to single-signal analysis, this chapter evaluates three physiological modalities, galvanic skin response (GSR), heart rate (HR), and their combined fusion, using the OhioT1DM 2018 dataset. We develop an end-to-end pipeline that integrates advanced preprocessing, temporal windowing, handcrafted and sequence-based feature extraction, early and late fusion strategies, and a broad spectrum of machine learning and deep temporal models, including CNNs, LSTMs, GRUs, and TCNs. Our results demonstrate that physiological signals exhibit distinct autonomic patterns preceding hypoglycemia and that combining GSR with HR consistently enhances detection sensitivity and stability compared to single-signal models. Multimodal deep learning architectures achieve the most reliable performance, particularly in recall, the most clinically urgent metric. Ablation studies further highlight the complementary contributions of each modality, strengthening the case for affordable, sensor-based glycemic monitoring. The findings show that real-time hypoglycemia detection is achievable using only inexpensive, non-invasive wearable sensors, offering a pathway toward accessible glucose monitoring in underserved communities and low-resource healthcare environments.