Abstract

During the COVID-19 situation, various application-based work has to be studied and deployed to enable an IoT-based health framework. This work-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic. Therefore, it identifies various technologies of IoT-based systems for monitoring pandemic situations. The mechanisms included in IoT like actuators, sensors, and the cloud-based network serves to help people from home rather than visiting the hospital occasionally. It uses optimizers to train the “noise” and “cough” target classes. Mel Frequency Cepstral Coefficients (MFCCs) were initially employed in several speech processing approaches, but as the discipline of Music Information Retrieval (MIR) advanced alongside machine learning, it was discovered that MFCCs could accurately capture timbre. Overall, the study finds different IoT applications for the medical area during the pandemic situation with detailed descriptions. In this present condition, advanced methodologies have given way to innovation in day-to-day life. The IoT-based model provides an enhancement of 98.8% with a minimum training loss of 0.15. The framework depicts the excellent working of the proposed framework, and a true positive value of around 96.6% is shown in the confusion matrix and a true negative rate of around 97% was illustrated using this model. By making it possible for the cost-effective fabrication of wearable sensors through printing on a variety of flexible polymeric substrates, the rapid advancements in solution-based nanomaterials presented a hopeful viewpoint to the field of wearable sensors. This review focuses on the most recent significant advancements in the field of wearable sensors, including novel nanomaterials, manufacturing techniques, substrates, sensor types, sensing mechanisms, and readout circuits. It concludes with difficulties in the subject's future application.

Full Text
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