通过移动传感器数据个性化了解血糖动态
Personalized Understanding of Blood Glucose Dynamics via Mobile Sensor Data
摘要：
连续血糖（CGM）监测仪彻底改变了糖尿病患者管理血糖的能力，并为人工胰腺系统铺平了道路。在本文中，我们用智能手机收集的传感器输入来增强CGM数据，并将其用于为患者和临床医生提供分析工具。我们使用自定义iOS应用程序收集了一名自由生活的1型糖尿病患者在9个月内的GPS数据、活动分类和血糖数据。该数据集在其大小、包含GPS数据以及从自由生活的患者非侵入性地收集的事实方面是新颖的。我们描述了一种方法来衡量发生的生活方式\textit{events}的基础上GPS和活动数据，并表明，他们可以捕获的情况下，食物消费，因此与血糖的变化。最后，我们将这些事件表示纳入我们的系统，以创建有用的可视化和通知，以帮助患者管理他们的糖尿病。
英文摘要：
Continuous Blood Glucose (CGM) monitors have revolutionized the ability of diabetics to manage their blood glucose, and paved the way for artificial pancreas systems. In this paper we augment CGM data with sensor input collected by a smart phone and use it to provide analytical tools for patients and clinicians. We collected GPS data, activity classifications, and blood glucose data with a custom iOS application over a 9 month period from a single free-living type-1 diabetic patient. This data set is novel in terms of it's size, the inclusion of GPS data, and the fact that it was collected non-intrusively from a free-living patient. We describe a method to measure the occurrence of lifestyle \textit{events} based on GPS and activity data, and show that they can capture instances of food consumption and are therefore correlated to changes in blood glucose. Finally, we incorporate these event representations into our system to create useful visualizations and notifications to aid patients in managing their diabetes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
