监测COVID-19大流行挑战的物联网可穿戴医疗系统

Thuria Saad Znad, I. A. A. Sayed, Saif Saad Hameed, I. Al-Barazanchi, P. S. JosephNg, A. Khalaf
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引用次数: 0

摘要

在2019冠状病毒病疫情期间,必须研究和部署各种基于应用的工作,以实现基于物联网的健康框架。这项基于工作的研究可以指导专业人员设想相关问题的解决方案,并与COVID-19型大流行作斗争。因此,它确定了基于物联网的系统的各种技术,以监测大流行情况。物联网中包含的机制,如执行器、传感器和基于云的网络,可以帮助人们在家,而不是偶尔去医院。它使用优化器来训练“噪音”和“咳嗽”目标类。Mel频率倒谱系数(mfcc)最初用于几种语音处理方法,但随着音乐信息检索(MIR)学科与机器学习一起发展,人们发现mfcc可以准确捕获音色。总体而言,该研究发现了疫情期间医疗领域的不同物联网应用,并进行了详细描述。在这种情况下,先进的方法已经让位于日常生活中的创新。基于物联网的模型提供了98.8%的增强,最小训练损失为0.15。该框架描述了所提出框架的出色工作,并且在混淆矩阵中显示了约96.6%的真正值,并且使用该模型说明了约97%的真负率。溶液基纳米材料的快速发展为可穿戴传感器领域的发展带来了希望,通过在各种柔性聚合物衬底上印刷,使成本效益高的可穿戴传感器制造成为可能。本文综述了可穿戴传感器领域的最新重大进展,包括新型纳米材料、制造技术、衬底、传感器类型、传感机制和读出电路。最后指出了该学科未来应用的难点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Internet of Things Wearable Healthcare System for Monitoring the Challenges of COVID-19 Pandemic
During the COVID-19 situation, various application-based work has to be studied and deployed to enable an IoT-based health framework. This work-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic. Therefore, it identifies various technologies of IoT-based systems for monitoring pandemic situations. The mechanisms included in IoT like actuators, sensors, and the cloud-based network serves to help people from home rather than visiting the hospital occasionally. It uses optimizers to train the “noise” and “cough” target classes. Mel Frequency Cepstral Coefficients (MFCCs) were initially employed in several speech processing approaches, but as the discipline of Music Information Retrieval (MIR) advanced alongside machine learning, it was discovered that MFCCs could accurately capture timbre. Overall, the study finds different IoT applications for the medical area during the pandemic situation with detailed descriptions. In this present condition, advanced methodologies have given way to innovation in day-to-day life. The IoT-based model provides an enhancement of 98.8% with a minimum training loss of 0.15. The framework depicts the excellent working of the proposed framework, and a true positive value of around 96.6% is shown in the confusion matrix and a true negative rate of around 97% was illustrated using this model. By making it possible for the cost-effective fabrication of wearable sensors through printing on a variety of flexible polymeric substrates, the rapid advancements in solution-based nanomaterials presented a hopeful viewpoint to the field of wearable sensors. This review focuses on the most recent significant advancements in the field of wearable sensors, including novel nanomaterials, manufacturing techniques, substrates, sensor types, sensing mechanisms, and readout circuits. It concludes with difficulties in the subject's future application.
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