基于开放api和地理定位的物联网系统,用于人类健康数据分析

O. Klochko, V. Fedorets, Maksym V. Mazur, Yurii P. Liulko
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引用次数: 2

摘要

基于开放API的应用程序开发在今天变得越来越流行。使用这些技术的创新项目为实时监测人体健康提供了新的机会。这些机会也通过物联网(IoT)、人工智能(AI)和云计算技术实现。在这项研究中,我们开发了一个基于开放api的应用程序,使用智能设备和气象地理信息系统,在此过程中生成有关空气中花粉(草花粉,桦树花粉和橄榄花粉)的存在对人类健康的危害的信息,表明其在空气中的浓度水平;空气质量问题,如果空气质量指标超过允许的标准。增加这类功能扩大了及时提供有关潜在风险和威胁的信息的可能性,因此,这是目前有效决策的“人-地理-数字”先决条件。该物联网系统的实施具有重要的方法和技术潜力,可用于提高医疗保健的效率,无论是在极端条件下还是在可持续存在的条件下。首先,这在COVID-19大流行期间和之后都是相关的。我们开发的系统也可以被视为医疗保健创新的途径之一,在高等教育机构的教育过程中,以及在这一主题的进一步科学研究中。该领域的进一步研究可能与基于机器学习、深度学习的医疗系统数据处理有关。
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An IoT system based on open APIs and geolocation for human health data analysis
Development of applications based on open API is becoming increasingly popular today. Innovative projects using these technologies provide new opportunities for real-time human health monitoring. Such opportunities are also implemented using Internet of Things (IoT), artificial intelligence (AI) and cloud computing technologies. In the study, we developed an application based on open APIs using smart gadgets and meteorological geographic information system in the process of generating a message about the dangers to human health associated with: the presence of pollen in the air (grass pollen, birch pollen and olive pollen) indicating the level of its concentration in the air; problems with air quality, if the air quality indicator exceeds the permissible standards. The addition of such functions expands the possibilities to provide timely information about potential risks and threats and, accordingly, is an "anthropo-geo-sensor-digital" prerequisite for effective decision-making, prevailing. The implementation of this IoT system has significant methodological and technological potential that can be used to improve the efficiency of Healthcare, both in extreme conditions and in conditions of sustainable existence. First of all, this is relevant during and after the COVID-19 pandemic. The system we have developed can also be seen as one of the ways to innovate in Healthcare, in the educational process in institutions of higher education and in further scientific research on this topic. Further research in this area may be related to data processing in Healthcare systems based on machine learning, deep learning.
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