Diabetes and extreme blood sugar levels are some of the major health problems faced by humans today across the world. While Continuous Glucose Monitoring (CGM) has emerged as an effective technology for management of diabetes as well as for monitoring blood sugar levels, this technology has traditionally been invasive (that is, requiring the piercing of the skin) and carries the risk of irritation, induration, etc. This highlights the need for accurate and non-invasive CGM methods that can be deployed at scale. With the emergence of various sensing technologies and their integration in wearables like the smart-watch, we now have the capability to continuously monitor body signals like the Photoplethysmogram (PPG) in a non-invasive manner. Having the ability to continuously monitor blood glucose through CGMs and continuously monitor PPG signals through a smart-watch offers an opportunity to get dense data on these two, opening the possibility of building machine learning and deep learning based models to estimate blood glucose level from PPG signals. In this work, we first present a paired dataset comprising continuous PPG signals from a smartwatch along with glucose values recorded using a CGM device. We also present the results of some preliminary experimental explorations performed on our dataset. These preliminary results suggest that some predictive signals may exist, though more exploration is needed with more data from a larger number of individuals. The dataset can be accessed at https://zenodo.org/records/20577959

翻译：糖尿病及极端血糖水平是人类当前面临的主要健康问题之一。虽然连续血糖监测（CGM）已成为糖尿病管理及血糖水平监测的有效技术，但该技术传统上需刺穿皮肤（即有创操作），且存在皮肤刺激、硬结等风险。因此，迫切需要可大规模部署的准确无创CGM方法。随着各类传感技术的涌现及其在智能手表等可穿戴设备中的集成，我们如今能以无创方式持续监测光电容积描记图（PPG）等人体信号。通过CGM连续监测血糖、通过智能手表持续记录PPG信号，使我们能够获取两类指标的密集数据，从而为构建基于机器学习和深度学习的PPG信号血糖估算模型提供了可能。本研究首先构建了一个配对数据集，其中包含来自智能手表的连续PPG信号及CGM设备记录的血糖值。我们还在该数据集上开展了初步实验探索，相关结果表明可能存在可预测性信号，但需通过更多人群的大规模数据进一步验证。数据集可通过https://zenodo.org/records/20577959获取。