基于物联网的COVID-19患者实时健康监测和氧气分配系统

S. Pingat, Harmeet Kaur Khanuja, Aayush Gavande, Chinmay Mahagaonkar
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引用次数: 0

摘要

在印度等发展中国家,鉴于2019冠状病毒病大流行等医疗危机,有效利用资源和基础设施至关重要。由于医院过度拥挤和医疗基础设施不足,传统的检查和监测病人的方法是无效的。对于COVID-19等慢性阻塞性肺疾病(COPDs)的治疗,监测患者的SpO2水平和脉搏率至关重要。本文侧重于使用物联网设备记录患者的基本特征,并对其进行数据分析,以进行未来预测。脉搏血氧计传感器用于获取患者的SpO2水平和脉搏率测量。该传感器输出由Wi-Fi SoC NodeMCU处理。通过对每个患者的唯一标识,这些数据通过移动应用程序显示给附近的医护人员。通过分析病人的症状,医生可以用同样的移动应用程序远程调节病人的氧气供应。机器学习算法被训练来分析和预测病人未来的健康状况。采用这种系统,现有的医疗结构可以在COVID-19等医疗危机期间大大提高效率和能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real Time Health Monitoring and Oxygen Distribution System for COVID-19 patients using IoT
In developing countries such as India, efficient use of resources and infrastructure is crucial in the light of healthcare crises such as the COVID-19 pandemic. Owing to overcrowded hospitals and inadequate medical infrastructure, traditional ways of examining and monitoring patients are ineffective. For the treatment of Chronic obstructive pulmonary diseases (COPDs) like COVID-19, monitoring a patient's SpO2 level along with the pulse rate is vital. This paper focuses on using IoT devices for documenting essential patient characteristics and performing data analytics on them for future predictions. Pulse oximeter sensor is used to obtain the patient's SpO2 level and pulse rate measurements. This sensor output is processed by Wi-Fi SoC NodeMCU. By unique identification of each patient, this data is displayed via a Mobile application to healthcare workers nearby. By analysing a patient's symptoms, a doctor can remotely regulate the supply of oxygen to the patient with the same mobile application. Machine learning algorithm is trained to analyse and predict a patient's future health conditions. With the adoption of such systems, the existing medical structure could improve vastly in its efficiency and capabilities during a healthcare crisis such as COVID-19.
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