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Open AccessArticle

Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections

1
Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patra, Greece
2
Department of Digital Systems, University of Thessaly, 41500 Larisa, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Paolo Bellavista
Received: 8 January 2021 / Revised: 29 January 2021 / Accepted: 3 February 2021 / Published: 7 February 2021
(This article belongs to the Special Issue Real-Time Systems in Emerging IoT-Embedded Applications)
Remote symptom tracking is critical for the prevention of Covid-19 spread. The qualified medical staff working in the call centers of primary health care units have to take critical decisions often based on vague information about the patient condition. The congestion and the medical protocols that are constantly changing often lead to incorrect decisions. The proposed platform allows the remote assessment of symptoms and can be useful for patients, health institutes and researchers. It consists of mobile desktop applications and medical sensors connected to cloud infrastructure. The unique features offered by the proposed solution are: (a) dynamic adaptation of Medical Protocols (MP) is supported (for the definition of alert rules, sensor sampling strategy and questionnaire structure) covering different medical cases (pre- or post-hospitalization, vulnerable population, etc.), (b) anonymous medical data can be statistically processed in the context of the research about an infection such as Covid-19, (c) reliable diagnosis is supported since several factors are taken into consideration, (d) the platform can be used to drastically reduce the congestion in various healthcare units. For the demonstration of (b), new classification methods based on similarity metrics have been tested for cough sound classification with an accuracy in the order of 90%. View Full-Text
Keywords: symptom tracking; mobile app; cloud; classification; similarity; sound processing symptom tracking; mobile app; cloud; classification; similarity; sound processing
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MDPI and ACS Style

Petrellis, N.; Adam, G.K. Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections. Computers 2021, 10, 22. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020022

AMA Style

Petrellis N, Adam GK. Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections. Computers. 2021; 10(2):22. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020022

Chicago/Turabian Style

Petrellis, Nikos; Adam, George K. 2021. "Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections" Computers 10, no. 2: 22. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10020022

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