The e-CoVig was one of the projects submitted by a group of researchers in the field of Biomedical engineering, led by the Instituto de Sistemas e Robótica (ISR), an associated institute of Instituto Superior Técnico (IST), and by researchers from Centro Cardiovascular of the University of Lisbon (CCUL) and the Faculty of Medicine, IT, Escola de Tecnologia de Saúde of Coimbra(EsteC) and BrainAnswer.
The project was designed following FCT's call for solutions in response to the Covid-19 pandemic, proposing an automated remote surveillance system for symptomatology associated with this disease. The three-month project was already accepted for funding and will start on May 1 and be concluded in the end of July.
Based on technology developed in recent years, e-CoVig intends to implement a low-cost technological solution for rigorous and real-time monitoring of Covid-19 symptoms, namely temperature and respiratory function, in addition to other data with clinical relevance for the diagnosis. Hugo Silva is the researcher from IT involved in the e-CoVig project, whose main contribution is at the level of the instrumentation and acquisition of biomedical signals.
The proposed platform consists of simultaneous pairing of an eHealth physiological data management platform with multiple data acquisition systems based on mobile phones of subjects in Covid 19 clinical surveillance, in isolation or not.
According to the project coordinator, Prof. João Sanches from IST, these measurements are very important in monitoring subjects who are in quarantine or have been diagnosed with the disease because these two conditions imply situations of extreme home isolation and deprivation of liberty that are strongly dependent on a rigorous symptom assessment. It is therefore a matter of minimizing restrictions on the individual freedom of those affected by this pathology.
This technology will support the Health authorities in managing the infected and will have a direct impact on the management of contact lines such as SNS24. The main objective is to give more fludity and automate the interactions between patients and the National Health System, reduce the risk of contamination in health professionals, densify the long-term monitoring process, increase the accuracy of the diagnosis, increase the capacity to monitor more subjects simultaneously and to generate automatic alerts.