Abstract
Goal: Millions of people are dying due to res- piratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symp- toms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches – unguided, semi-guided, and guided approaches consid- ering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Con- clusions: Though guided models outperform other models, they require a better understanding of the environment.
Keywords
COVID-19, Smart phones, Analytical models, Performance evaluation, Audio recording, Adaptation models, Support vector machines
Date of this Version
4-28-2023
Comments
This is the published version of S. Vhaduri, S. V. Dibbo and Y. Kim, "Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings," in IEEE Open Journal of Engineering in Medicine and Biology, vol. 4, pp. 55-66, 2023, doi: 10.1109/OJEMB.2023.3271457.