A Survey of Healthcare Internet of Things (HIoT): A Clinical Perspective

被引:2
|
作者
Habibzadeh, Hadi [1 ]
Dinesh, Karthik [2 ]
Shishvan, Omid Rajabi [1 ]
Boggio-Dandry, Andrew [1 ]
Sharma, Gaurav [2 ]
Soyata, Tolga [1 ]
机构
[1] SUNY Albany, Dept Elect & Comp Engn, Albany, NY 12203 USA
[2] Univ Rochester, Dept Elect & Comp Engn, Rochester, NY 14627 USA
基金
美国国家科学基金会;
关键词
Medical services; Sensors; Internet of Things; Market research; Security; Biomedical monitoring; Monitoring; Clinical Internet of Things (IoT); digital health; health management; health monitoring; healthcare analytics; medical decision support; WIRELESS SENSOR NETWORKS; DISEASE RATING-SCALE; BIG DATA; COMMUNICATION TECHNOLOGIES; ACTIVITY RECOGNITION; REGIMEN ADHERENCE; DETECTION SYSTEM; OF-THINGS; IOT; ARCHITECTURE;
D O I
10.1109/JIOT.2019.2946359
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In combination with current sociological trends, the maturing development of Internet of Things devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data, that is, orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, Healthcare Internet of Things data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this article, we survey the existing and emerging technologies that can enable this vision for the future of healthcare, particularly, in the clinical practice of healthcare. Three main technology areas underlie the development of this field: 1) sensing, where there is an increased drive for miniaturization and power efficiency; 2) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure; and 3) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout this article, we use a case study to concretely illustrate the impact of these trends. We conclude this article with a discussion of the emerging directions, open issues, and challenges.
引用
收藏
页码:53 / 71
页数:19
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