• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

使用可穿戴传感器实现无痛血糖监测:最新进展和未来展望。

Pain-Free Blood Glucose Monitoring Using Wearable Sensors: Recent Advancements and Future Prospects.

出版信息

IEEE Rev Biomed Eng. 2018;11:21-35. doi: 10.1109/RBME.2018.2822301. Epub 2018 Apr 2.

DOI:10.1109/RBME.2018.2822301
PMID:29993663
Abstract

Keeping track of blood glucose levels non-invasively is now possible due to diverse breakthroughs in wearable sensors technology coupled with advanced biomedical signal processing. However, each user might have different requirements and priorities when it comes to selecting a self-monitoring solution. After extensive research and careful selection, we have presented a comprehensive survey on noninvasive/pain-free blood glucose monitoring methods from the recent five years (2012-2016). Several techniques, from bioinformatics, computer science, chemical engineering, microwave technology, etc., are discussed in order to cover a wide variety of solutions available for different scales and preferences. We categorize the noninvasive techniques into nonsample- and sample-based techniques, which we further grouped into optical, nonoptical, intermittent, and continuous. The devices manufactured or being manufactured for noninvasive monitoring are also compared in this paper. These techniques are then analyzed based on certain constraints, which include time efficiency, comfort, cost, portability, power consumption, etc., a user might experience. Recalibration, time, and power efficiency are the biggest challenges that require further research in order to satisfy a large number of users. In order to solve these challenges, artificial intelligence (AI) has been employed by many researchers. AI-based estimation and decision models hold the future of noninvasive glucose monitoring in terms of accuracy, cost effectiveness, portability, efficiency, etc. The significance of this paper is twofold: first, to bridge the gap between IT and medical field; and second, to bridge the gap between end users and the solutions (hardware and software).

摘要

由于可穿戴传感器技术与先进的生物医学信号处理技术的多样化突破,现在可以无创地跟踪血糖水平。然而,每个用户在选择自我监测解决方案时可能有不同的要求和优先级。经过广泛的研究和仔细的选择,我们对最近五年(2012-2016 年)的无创/无痛苦血糖监测方法进行了全面调查。从生物信息学、计算机科学、化学工程、微波技术等多个领域讨论了几种技术,以涵盖适用于不同规模和偏好的各种解决方案。我们将无创技术分为无样本和基于样本的技术,并进一步将其分为光学、非光学、间歇和连续。本文还比较了用于无创监测的制造或正在制造的设备。然后根据某些约束条件(包括时间效率、舒适度、成本、便携性、功耗等)对这些技术进行分析,用户可能会遇到这些约束条件。重新校准、时间和功率效率是需要进一步研究以满足大量用户需求的最大挑战。为了解决这些挑战,许多研究人员都采用了人工智能(AI)。基于人工智能的估计和决策模型在准确性、成本效益、便携性、效率等方面代表了无创血糖监测的未来。本文的意义有两个方面:一是弥合 IT 与医疗领域之间的差距;二是弥合最终用户与解决方案(硬件和软件)之间的差距。

相似文献

1
Pain-Free Blood Glucose Monitoring Using Wearable Sensors: Recent Advancements and Future Prospects.使用可穿戴传感器实现无痛血糖监测:最新进展和未来展望。
IEEE Rev Biomed Eng. 2018;11:21-35. doi: 10.1109/RBME.2018.2822301. Epub 2018 Apr 2.
2
Wearable Skin Sensors and Their Challenges: A Review of Transdermal, Optical, and Mechanical Sensors.可穿戴皮肤传感器及其挑战:对透皮、光学和机械传感器的综述。
Biosensors (Basel). 2020 May 28;10(6):56. doi: 10.3390/bios10060056.
3
Wearable Electrochemical Glucose Sensors in Diabetes Management: A Comprehensive Review.可穿戴电化学葡萄糖传感器在糖尿病管理中的应用:全面综述。
Chem Rev. 2023 Jun 28;123(12):7854-7889. doi: 10.1021/acs.chemrev.3c00078. Epub 2023 May 30.
4
Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms.基于手腕可穿戴传感器的无创特征选择,使用机器学习算法预测血糖浓度。
Sensors (Basel). 2022 May 6;22(9):3534. doi: 10.3390/s22093534.
5
Wearable Electrochemical Sensors for the Monitoring and Screening of Drugs.用于药物监测和筛查的可穿戴式电化学传感器。
ACS Sens. 2020 Sep 25;5(9):2679-2700. doi: 10.1021/acssensors.0c01318. Epub 2020 Aug 21.
6
The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review.利用人工智能的可穿戴设备进行血糖水平预测的有效性:系统评价。
J Med Internet Res. 2023 Mar 14;25:e40259. doi: 10.2196/40259.
7
Flexible Wearable Sensors for Cardiovascular Health Monitoring.用于心血管健康监测的灵活可穿戴传感器。
Adv Healthc Mater. 2021 Sep;10(17):e2100116. doi: 10.1002/adhm.202100116. Epub 2021 May 6.
8
First Experiences With a Wearable Multisensor in an Outpatient Glucose Monitoring Study, Part I: The Users' View.门诊血糖监测研究中可穿戴多传感器的首次体验,第一部分:用户视角
J Diabetes Sci Technol. 2018 May;12(3):562-568. doi: 10.1177/1932296817750932. Epub 2018 Jan 14.
9
Wearable non-invasive epidermal glucose sensors: A review.可穿戴式无创表皮葡萄糖传感器:综述。
Talanta. 2018 Jan 15;177:163-170. doi: 10.1016/j.talanta.2017.08.077. Epub 2017 Aug 30.
10
Review of Noninvasive Continuous Glucose Monitoring in Diabetics.糖尿病无创连续血糖监测的回顾。
ACS Sens. 2023 Oct 27;8(10):3659-3679. doi: 10.1021/acssensors.3c01538. Epub 2023 Oct 16.

引用本文的文献

1
Wearable Devices & Elderly: A Bibliometric Analysis of 2014-2024.可穿戴设备与老年人:2014 - 2024年文献计量分析
Healthcare (Basel). 2025 Aug 20;13(16):2066. doi: 10.3390/healthcare13162066.
2
Principal Component Analysis Based Quaternion-Valued Medians for Non-Invasive Blood Glucose Estimation.基于主成分分析的四元数中值用于无创血糖估计
Sensors (Basel). 2025 Jun 15;25(12):3746. doi: 10.3390/s25123746.
3
Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.
机器学习与人工智能在2型糖尿病预测中的应用:一项为期33年的全面文献计量学与文献分析
Front Digit Health. 2025 Mar 27;7:1557467. doi: 10.3389/fdgth.2025.1557467. eCollection 2025.
4
AI-Driven Management of Type 2 Diabetes in China: Opportunities and Challenges.中国2型糖尿病的人工智能驱动管理:机遇与挑战
Diabetes Metab Syndr Obes. 2025 Jan 8;18:85-92. doi: 10.2147/DMSO.S495364. eCollection 2025.
5
Advances in Wearable Biosensors for Healthcare: Current Trends, Applications, and Future Perspectives.可穿戴生物传感器在医疗保健领域的进展:当前趋势、应用和未来展望。
Biosensors (Basel). 2024 Nov 18;14(11):560. doi: 10.3390/bios14110560.
6
A comprehensive review on electromagnetic wave based non-invasive glucose monitoring in microwave frequencies.基于微波频率的电磁波无创血糖监测综合综述。
Heliyon. 2024 Sep 11;10(18):e37825. doi: 10.1016/j.heliyon.2024.e37825. eCollection 2024 Sep 30.
7
Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine.用于个性化疼痛医学的人工智能引导的传感器与设备
Cyborg Bionic Syst. 2024 Sep 13;5:0160. doi: 10.34133/cbsystems.0160. eCollection 2024.
8
Microfluidic-based systems for the management of diabetes.基于微流控的糖尿病管理系统。
Drug Deliv Transl Res. 2024 Nov;14(11):2989-3008. doi: 10.1007/s13346-024-01569-y. Epub 2024 Mar 20.
9
Integrated electronic/fluidic microneedle system for glucose sensing and insulin delivery.集成电子/流体微针系统用于葡萄糖感测和胰岛素输送。
Theranostics. 2024 Feb 11;14(4):1662-1682. doi: 10.7150/thno.92910. eCollection 2024.
10
Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review.基于人工智能的消费者健康信息学应用:范围综述。
J Med Internet Res. 2023 Aug 30;25:e47260. doi: 10.2196/47260.