IEEE Open Journal of Engineering in Medicine and Biology（2023）
引用3|浏览0
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摘要
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.
