  Harnessing the power of Artificial Intelligence (AI) and m-health towards
detecting new bio-markers indicative of the onset and progress of respiratory
abnormalities/conditions has greatly attracted the scientific and research
interest especially during COVID-19 pandemic. The smarty4covid dataset contains
audio signals of cough (4,676), regular breathing (4,665), deep breathing
(4,695) and voice (4,291) as recorded by means of mobile devices following a
crowd-sourcing approach. Other self reported information is also included (e.g.
COVID-19 virus tests), thus providing a comprehensive dataset for the
development of COVID-19 risk detection models. The smarty4covid dataset is
released in the form of a web-ontology language (OWL) knowledge base enabling
data consolidation from other relevant datasets, complex queries and reasoning.
It has been utilized towards the development of models able to: (i) extract
clinically informative respiratory indicators from regular breathing records,
and (ii) identify cough, breath and voice segments in crowd-sourced audio
recordings. A new framework utilizing the smarty4covid OWL knowledge base
towards generating counterfactual explanations in opaque AI-based COVID-19 risk
detection models is proposed and validated.
