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

2026-02-11

AI Summary

This work proposes a non-invasive, real-time hypoglycemia detection method based on multimodal physiological signals for settings where costly or invasive continuous glucose monitoring devices are unavailable. By fusing galvanic skin response (GSR) and heart rate (HR) signals within an end-to-end deep learning framework, the study systematically evaluates the performance of both unimodal and multimodal fusion strategies. Advanced preprocessing, temporal windowing, and a combination of handcrafted and sequential feature extraction are employed alongside early and late fusion mechanisms, integrating temporal models such as CNNs, LSTMs, GRUs, and TCNs. Experimental results demonstrate that the proposed multimodal approach significantly improves recall in hypoglycemia detection, confirming the clinical feasibility and complementary advantages of low-cost wearable sensors for achieving high-sensitivity and stable early warning.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• hypoglycemia detection
• non-invasive monitoring
• wearable sensors
• diabetes management
• affordable healthcare

Innovation

• multimodal fusion
• non-invasive hypoglycemia detection
• wearable sensors
• deep temporal models
• affordable glucose monitoring