大流行期间健康监测的电子保健和智能解决方案框架

János Dávid Balogh, A. Adamkó
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引用次数: 0

摘要

我们的主要目标是关注应用程序的不同设计原则,这些应用程序可以接收和存储来自智能设备的各种数据,例如智能手环、智能手表、智能家居,并可以在一定程度上获得有关其用户健康的更多信息。我们希望收集这些信息,存储它们,然后创建易于理解的图表,并在一个现代的、响应式的环境中向用户展示它们。我们把重点放在架构上,它拥有必要的特性(可扩展性和可扩展性)。在设计过程中,我们遵循标准、建议和协议。其结果是一个应用程序框架,它履行了XXI的职责。世纪智能解决方案的应用,可以监测人们的健康状况,甚至有助于过上更健康的生活,避免慢性疾病,如肥胖和/或糖尿病。根据收集到的信息和研究成果,重点对COVID-19疾病进行预测和分类。我们使用自己的设备完成了这个项目,比如三星Galaxy Watch 2,它集成了Tizen操作系统,并有机会在其上开发数据发送应用程序。另一方面,我们与智能手环合作,如小米小米手环6,但它没有可编程的操作系统,所以我们需要找到一个解决方案,从它收集数据,但他的案例在后面的论文中讨论。我们想在我校诊所现场实施这个项目,以获得更多的真实病例和数据。我们正在研究一种解决方案,让每个人都有自己的医疗助理来预测和预防医疗问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
eHealth and Smart Solutions framework for health monitoring in the course of the pandemic
Our main objective focusing on the different design principles for applications which can receiveand store various data from smart devices e.g., smart bands, smart watches, smart homes and could gainmore information about its users with some extent to their health. We would like to collect this information,store them, then create well understandable diagrams and show them to the user in a modern, responsiveenvironment. We placed great em- phasis on the architecture which holds the necessary features (scalabilityand extensibility). We have followed the standards, recommendations, and protocols during the designprocess. The result is an application framework which fulfills the duty of an XXI. century smart solutionsapplication that could monitor the health of people and even help to live a healthier life avoiding chronicdisorders e.g., obesity and/or diabetes. We focused on predicting and classifying on COVID-19 disease tooaccording to the collected information and research results. We achieved tihs project using our own deviceslike a Samsung Galaxy Watch 2 whch comes integrated Tizen OS and gave the opportunity to develop adata sending application on it. On the other hand we worked with smartbands like the Xiaomi Mi Band 6but it does not have programable OS so we needed to find a solution to collect data from it but his case isdiscussed in the paper later. We would like to implement this project live with the Clinic of our universityto get more real cases and data. We are working on a solution where everyone could have their own medicalassistant to predict and prevent healthcare issues.
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