ArticlePervasive and mobile computing2023
Transfer learning for the efficient detection of COVID-19 from smartphone audio data.
Article in Pervasive and mobile computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Chronic obstructive pulmonary disease screening using time-frequency features of self-recorded respiratory sounds.JAMIA open · 2025Article
- A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation.Journal of medical Internet research · 2025Article
- Sound-Event Detection of Water-Usage Activities Using Transfer Learning.Sensors (Basel, Switzerland) · 2023Article
- COVID-19 Detection from Cough Recordings Using Bag-of-Words Classifiers.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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Abstract
Disease detection from smartphone data represents an open research challenge in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are an important case study in this area and their early detection is a potential real instrument to counteract the pandemic situation. The efficacy of this solution mainly depends on the performances of AI algorithms applied to the collected data and their possible implementation directly on the users' mobile devices. Considering these issues, and the limited amount of available data, in this paper we present the experimental evaluation of 3 different deep learning models, compared also with hand-crafted features, and of two main approaches of transfer learning in the considered scenario: both feature extraction and fine-tuning. Specifically, we considered VGGish, YAMNET, and L
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