Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings.

Goal: Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms 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 considering 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. Conclusions: Though guided models outperform other models, they require a better understanding of the environment.

Understanding user concerns and choice of app architectures in designing Audio-based mHealth Apps

Due to recent advancements in smartphone sensing and computing capabilities, artificially intelligent mobile health (mHealth) applications are getting popular to monitor a range of diseases, including coronavirus-caused COVID-19 disease, chronic obstructive pulmonary disease (COPD), asthma, bronchitis, emphysema, and sleep apnea, among many others utilizing the audio recordings obtained from the smartphone microphones. Compared to other mHealth apps, audio-based apps suffer from various user concerns, including the privacy of data and battery drain rate, among many other concerns, which can adversely affect the user compliance, and app utility and life cycle. To address user concerns, mHealth apps should provide users options to configure the app, i.e., choose an architecture from a set of options based on user concerns and preferences. However, there is a dearth of knowledge about audio-based health monitoring app design. In this work, we present a focused user-centric app design study to better understand various concerns and choices that users want to see in an audio-based mHealth app. From a detailed analysis of 60 subjects with varying backgrounds, we find that around 85% subjects are concerned about the privacy of data and 93% subjects prefer to pick an app architecture that will not send raw audio recordings to a server. Findings from this work can guide the design of future mHealth apps that utilizes privacy-sensitive audio data.