ABSTRACT
With the proliferation of COVID-19 cases, it has become indispensable to conceive of innovative solutions to abate the mortality count due to the pandemic. With a steep rise in daily cases, it is a known fact that the current testing capacity is a major hindrance in providing the right healthcare for the individuals. The common methods of detection include swab tests, blood test results, CT scan images, and using cough sounds paired with AI. The unavailability of data for the application of deep learning techniques has proved to be a major issue in the development of deep learning-enabled solutions. In this work, a novel solution of a screening device that is capable of collecting audio samples and utilizing deep learning techniques to predict the probability of an individual to be diagnosed with COVID-19 is proposed. The model is trained on public datasets, which is to be manually examined and processed. Audio features are extracted to create a dataset for the model which will be developed using the TensorFlow framework. The trained model is deployed on an ARM CortexM4 based nRF52840 microcontroller using the lite version of the model. The in-built PDM-based microphone is to be used to capture the audio samples. The captured audio sample will be used as an input for the model for screening. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.