An Internet of Things (IoT) Management System for Improving Homecare - A Case Study

Areej Almazroa, Hongjian Sun
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引用次数: 1

Abstract

Due to the increasing of population, the number of hospital visits by patients are increasing which puts a pressure on hospitals. Nowadays, the need for taking care of patients while they are at home is essential. Internet of Things (IoT) has been widely used in different areas such as healthcare and smart homes. IoT will assist in minimizing the hospital burden of frequent patients' visits. Applying IoT in healthcare will improve the efficiency and effectiveness, bring economic benefits, and reduce human exertions. It is well known that the best health monitoring system is able to detect abnormalities and able to make diagnosis without human exertion. However, this kind of system is dealing with health conditions which are essential and sensitive that require high accuracy to be reliable. This paper presents an Electrocardiogram (ECG) monitoring framework that overcome the accuracy limitation. Signal processing and feature extraction are applied. For the diagnosis purpose a classification stage is made in two ways; threshold values and machine learning to increase the accuracy. Experiment results reveal that the proposed model is more accurate in the diagnosis of heart diseases than other researches which makes it more confident to rely on from health experts point of view.
改善家庭护理的物联网(IoT)管理系统-案例研究
由于人口的增长,患者的就诊次数不断增加,这给医院带来了压力。如今,照顾病人在家是必不可少的。物联网(IoT)已广泛应用于医疗保健和智能家居等不同领域。物联网将有助于减少患者频繁就诊的医院负担。将物联网应用于医疗保健将提高效率和效果,带来经济效益,减少人力劳动。众所周知,最好的健康监测系统是能够检测异常并能够在不需要人类努力的情况下做出诊断。然而,这种系统处理的是基本和敏感的健康状况,需要高精度才能可靠。本文提出了一种克服精度限制的心电图监测框架。应用了信号处理和特征提取。为了诊断目的,分类阶段分为两种方式;阈值和机器学习来提高准确性。实验结果表明,该模型在心脏病诊断方面比其他研究更准确,从健康专家的角度来看,更有信心。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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