A 2-in-1 Vital Sign Monitor with Smart Fever Type Classification for Home-Care Services

Nurul Izzati Darul Zaman, Y. Hau, R. Al-ashwal, M. Leong
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Abstract

Monitoring of vital sign parameters is crucial in identifying clinical deterioration. Therefore, these parameters must be measured regularly to monitor acute and chronic diseases. However, most of the existing vital sign monitors are designed mainly for hospital clinical settings. These devices are generally bulky, with complex user interfaces and no integration of mobile applications, as well as auto fever-type classification algorithms for potential disease screening. As a result, this paper presents a 2-in-1 home-based vital sign monitor based on an Arduino Nano microcontroller, a DS18B20 temperature sensor to measure body temperature from the fingertip, and an MPX5050DP pressure sensor to acquire blood pressure based on the oscillometric method. An Android mobile application is also developed and integrated with the device through wireless Bluetooth to display vital sign measurement results, analyze fever patterns and symptoms for early detection of potential diseases, and send an alert notification message if abnormalities are detected. Google Firebase Authentication is also deployed for user authentication. The results show that the system provides an accurate vital sign measurement of 98.75% and 99.94% for blood pressure and body temperature, respectively. The mobile application was also successfully deployed with a fever classification algorithm as a clinical decision support system.Clinical Relevance— The vital signs and fever classification result can be used as part of a clinical decision support system by clinicians for infectious disease screening.
一种二合一生命体征监测仪,具有智能发热类型分类,适用于家庭护理服务
监测生命体征参数是识别临床恶化的关键。因此,必须定期测量这些参数,以监测急性和慢性疾病。然而,大多数现有的生命体征监测仪主要是为医院临床环境设计的。这些设备通常体积庞大,用户界面复杂,没有集成移动应用程序,也没有用于潜在疾病筛查的自动发烧类型分类算法。因此,本文提出了一种基于Arduino Nano微控制器的二合一家用生命体征监测仪,采用DS18B20温度传感器从指尖测量体温,采用MPX5050DP压力传感器基于示波法采集血压。此外,还开发了Android移动应用程序,通过无线蓝牙与设备集成,显示生命体征测量结果,分析发烧模式和症状,早期发现潜在疾病,并在发现异常时发送警报通知信息。谷歌还部署了Firebase Authentication对用户进行身份验证。结果表明,该系统对血压和体温的测量准确率分别为98.75%和99.94%。该移动应用程序还成功地部署了发烧分类算法作为临床决策支持系统。临床相关性-生命体征和发烧分类结果可作为临床医生用于传染病筛查的临床决策支持系统的一部分。
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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