ABSTRACT
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. © 2021 IEEE.