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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.

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。

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