Scholarlink

<p>Diabetes is a major health challenge that affects millions of people worldwide. Managing diabetes effectively requires monitoring blood glucose levels continuously, typically through invasive sensing devices such as continuous glucose monitors (CGMs). Blood glucose excursions have been shown to induce changes in several physiological signals such as electrocardiography (ECG), photoplethysmography (PPG) and electrodermal activity (EDA) that can be measured non-invasively with consumer-grade wearable sensors. These physiological changes can be mapped into glucose levels using machine-learning models trained on comprehensive multimodal datasets of physiological signals. However, most existing datasets lack ground-truth measurements from CGMs and often only include aggregated physiological data (e.g. heart rate) at low sampling rates. To address these gaps, we introduce PhysioCGM, an open-source dataset that contains raw physiological recordings from multiple sensors including ECG, PPG, EDA, skin temperature, accelerometry and ground-truth CGM data collected for up to 17 days from 10 participants with Type 1 Diabetes in ambulatory settings. This dataset aims to promote the development of non-invasive methods for glucose monitoring and improve diabetes management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A multimodal physiological dataset for non-invasive blood glucose estimation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月19日

查看全文
  • Waris Quamer,
  • Mu-Ruei Tseng,
  • Kathan Vyas,
  • Darpit Dave,
  • Carolina Villegas,
  • Siripoom McKay,
  • Daniel J. DeSalvo,
  • Madhav Erranguntla,
  • Gerard Cote,
  • Ricardo Gutierrez-Osuna

摘要

Diabetes is a major health challenge that affects millions of people worldwide. Managing diabetes effectively requires monitoring blood glucose levels continuously, typically through invasive sensing devices such as continuous glucose monitors (CGMs). Blood glucose excursions have been shown to induce changes in several physiological signals such as electrocardiography (ECG), photoplethysmography (PPG) and electrodermal activity (EDA) that can be measured non-invasively with consumer-grade wearable sensors. These physiological changes can be mapped into glucose levels using machine-learning models trained on comprehensive multimodal datasets of physiological signals. However, most existing datasets lack ground-truth measurements from CGMs and often only include aggregated physiological data (e.g. heart rate) at low sampling rates. To address these gaps, we introduce PhysioCGM, an open-source dataset that contains raw physiological recordings from multiple sensors including ECG, PPG, EDA, skin temperature, accelerometry and ground-truth CGM data collected for up to 17 days from 10 participants with Type 1 Diabetes in ambulatory settings. This dataset aims to promote the development of non-invasive methods for glucose monitoring and improve diabetes management.