Empowering pandemic resilience: simulation of integrating IoT innovation to curtail mortality - Singh, Spoorthi; Zuber, Mohammad; Hegde, Navya Thirumaleshwar; Jain, Meet Hitesh; Hamidon, Mohd Nizar; Basri, Adi Azriff; Mazlan, Norkhairunnisa; Ahmad, Kamarul Arifin; Moorthy, Ramya S (Elsevier B.V., 2025)
Historically Pandemic and Epidemic Prone (PEP) diseases rapidly inflame the respiratory tract and influence heart rate very badly, like how corona virus 2019 was transmitted by SARS-CoV-2. Thus, this article proposes a conceptual design that considers heart rate change, and respiration rate variations measured are indicated through IoT with a mobile application. Here the app-based method solves the connectivity gap through technology infusion, which was needed during pandemic. Since wearable devices to measure heart rate/ respiratory rate are commonly available, here the proposal suggests one such best-suited wearable strain sensor to focus on respiration rate and volume of a human respiratory system with high fidelity for servicing PEP patients. The proposed concept can help in monitoring and tracking multiple patients simultaneously, online through Internet of Things (IoT). This IoT enabled app not only updates doctors on already discharged patients, but also can be used by anyone to prevent last minute uncertainties. Since respiratory issues being one of the main symptoms of patients recovered from PEP disease, tracking the breathing patterns is essential. This issue requires an effective sensorics system and associated software applications, thus connecting hospital administration to the public. The aim is to help the public use a single software application to access the ambulance, hospital, and doctors, reducing time via data transfer leveraging IoT. This has been simulated using Cisco packet tracer for fast processing of data and the results are observed. As a pre-requisite of the application respiratory rate data set has been converted and the required alert indications have been shown after comparing with reference data set.