基于物联网的射频识别心力衰竭监测系统

Syahirah Asyiqin Alias, D. Mohd Ali, Yusmardiah Yusuf
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引用次数: 1

摘要

本文介绍了一种基于物联网(IoT)的使用射频识别(RFID)的心力衰竭监测系统。它的开发是为了帮助医生远程监测患有心力衰竭疾病的患者的健康状况。监测过程可以在没有医院在场的情况下完成,特别是在COVID-19大流行危机期间,医生和护理人员可以在需要时访问患者的医疗记录。本课题采用Arduino Mega作为微控制器,接口有温度传感器、脉搏血氧计传感器、脉搏传感器等多个传感器作为输入。在监测过程中,患者只需要将指尖放在每个传感器上,即可获得健康数据的读数。这些数据将被发送到微控制器进行处理,并与每个传感器的阈值进行比较。如果获得的数据超过阈值,则通过Blynk移动应用程序发送通知,通知医生或护理人员患者的健康状况异常,以便采取进一步行动。ESP8266 Wi-Fi模块允许医生使用Blynk移动应用程序从云服务器访问和监控患者的数据。这个移动应用程序实时显示和通知患者的健康状况。此外,健康数据还可以导出到Excel中进行分析和评估。测试结果表明,该系统在监测患者的健康状况方面取得了令人满意的结果,并几乎准确地读取了健康数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Internet of Things Based Heart Failure Monitoring System using Radio Frequency Identification
This paper presents an internet of things (IoT) based heart failure monitoring system using radio frequency identification (RFID). It is developed to assist doctors to monitor patient's health conditions with heart failure disease remotely. The monitoring process can be done without having the presence at the hospital especially during the COVID-19 pandemic crisis where the doctor and the caretaker can access the medical record of the patient whenever required. This project employs Arduino Mega as the microcontroller which is interfaced with several sensors as input such as temperature sensor, pulse oximeter sensor and pulse sensor. The patient only needs to place their fingertip on each sensor while the monitoring process is performed to obtain the reading of health data. These data will be sent to the microcontroller to be processed and compared with the threshold value of each sensor. If the data obtained exceeds the threshold value, a notification is sent via Blynk mobile application to notify the doctor or caretaker on the abnormalities of the patient's health condition for further action. ESP8266 Wi-Fi module is used to allow doctor to access and monitor their patient's data from the cloud server using Blynk mobile application. This mobile application displays and notify the health condition of the patient in real time. Besides, the health data also can be exported to Excel for analysis and evaluation. The result obtained from testing the system turned out to be satisfactory in monitoring patient's health condition with an almost accurate reading of health data.
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