利用物联网和可穿戴设备推动医疗监控:系统综述

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
João Baiense, Eftim Zdravevski, Paulo Coelho, Ivan Miguel Pires, Fernando Velez
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引用次数: 0

摘要

可穿戴技术已经成为医疗保健行业的重要组成部分,收集个人健康数据并提取有价值的信息以提供实时帮助。本综述分析了35篇关于利用物联网和可穿戴设备应用推动医疗保健监测的科学出版物。使用自然语言处理框架和PRISMA方法对这些论文进行定量和定性分析,以过滤搜索结果。所选论文发表于2010年1月至2024年5月之间,发表于以下科学数据库之一:IEEE Xplore, b施普林格,ScienceDirect(即Elsevier),美国计算机协会(ACM),多学科数字出版研究所(MDPI)或PubMed Central。该分析考虑了人口、方法、硬件、功能和通信。研究强调,从一个或多个传感器收集的数据在数据库服务器中进行处理和访问,用于各种用途,例如通知专业职业或协助用户。这篇综述表明,考虑到这篇综述中提出的有价值的原则,可以设计出强大而有效的驱动物联网和可穿戴设备应用的医疗监控。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Driving Healthcare Monitoring with IoT and Wearable Devices: A Systematic Review
Wearable technologies have become a significant part of the healthcare industry, collecting personal health data and extracting valuable information for real-time assistance. This review paper analyzes thirty-five scientific publications on driving healthcare monitoring with IoT and wearable device applications. These papers were considered in a quantitative and qualitative analysis using the Natural Language Processing framework and the PRISMA methodology to filter the search results. The selected papers were published between January 2010 and May 2024 in one of the following scientific databases: IEEE Xplore, Springer, ScienceDirect (i.e., Elsevier), Association for Computing Machinery (ACM), Multidisciplinary Digital Publishing Institute (MDPI), or PubMed Central. The analysis considers population, methods, hardware, features, and communications. The research highlights that data collected from one or numerous sensors is processed and accessible in a database server for various uses, such as informing professional careers or assisting users. The review suggests that robust and efficient driving healthcare monitoring with IoT and wearable devices applications can be designed considering the valuable principles presented in this review.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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