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Development of a wearable IoT device for Covid-19 early diagnosis

Nicola Elia

Development of a wearable IoT device for Covid-19 early diagnosis.

Rel. Edoardo Patti, Matteo Orlando. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021


The Covid-19 pandemic, also known as the "Coronavirus pandemic", started in December, 2019 in Wuhan (CN) and ongoing at the time of writing this thesis work, has caused a huge amount of damage in various aspects of the human society, resulting in political, social, economic implications. The World Health Organization declared a public health emergency of international concern; millions of lives came to an end, the largest global recession since the Great Depression occurred and a lot of social habits had to change in order to reduce the chances of infection. The Covid-19 responsible virus indeed spreads from person to person mainly through the respiratory route, that means through the respiratory droplets or aerosols, which get into the mouth, nose or eyes of other people when they are in contact with an infected person who talks, sings, coughs or sneezes. An infected person may have much variable, that span from headache to respiratory failure. Common symptoms include cough, loss of smell and taste, headache, nasal congestion, muscle pain, fever and breathing difficulties. The diagnosis of Covid-19 happens by testing for presence of SARS-CoV-2, which is the virus that causes the COronaVIrus Disease 2019. The most common method is the real-time reverse transcription polymerase chain reaction (rRT-PCR), typically done on nasopharyngeal swab samples to detect the presence of viral RNA fragments; each test requires some hours to be accomplished, thus resulting in a heavily resources and time-consuming practice. This is why prevention and early diagnosis are the two main anchors that governments hooked to in order to block the virus diffusion and the growing of outbreaks. Even though prevention and early diagnosis may appear as absolutely good practices, they often lead to drastic decisions that must be taken in favor of public health, but regardless of the side effects: closure of schools, closure of shopping centers, stop of production in industrial plants caused a lot of negative consequences from the social and economic points of view. For instance, lots of industries and restaurants has been temporarily closed due to outbreaks, with huge impact on their incomes. Moreover, the lack of preparation of the national healthcare system and the low availability of doctors and nurses led to two disastrous phenomena: the birth and growth of outbreaks in an uncontrolled way and the rapid saturation of the hospitals. In this tragic scenario, in which the availability of humans is drastically low, the idea of a smart autonomous tool for helping in large scale early diagnosis and monitoring was born. As explained before, Covid-19 infection manifests also through measurable symptoms. Scientific evidence exist that a significant percentage of the Covid-19 infected people show a common trend in SpO2 and heart-rate vitals. Moreover, the presence of fever can be verified by simply measuring the body temperature. As a consequence, monitoring a person's SpO2, HR and body temperature and analyzing their trend over the time can work as an early diagnosis mechanism for Covid-19 detection. Given that, the Internet of Things paradigms and techniques are the best candidates to accomplish the task of designing a device for logging people's vitals through sensors, streaming to cloud services in order to provide real-time monitoring of vitals. Relevant stakeholders may include: hospitals, for continuous monitoring of patients, saving nurses time, other companies,

Relators: Edoardo Patti, Matteo Orlando
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 147
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: VHIT S.p.A. Bosch Group
URI: http://webthesis.biblio.polito.it/id/eprint/18004
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