Related papers: Internet of Things and Health Care in Pandemic COVID-19: System Requirements Evaluation

Internet of Things and Health Care in Pandemic COVID-19: System Requirements Evaluation

URL: http://arxiv.org/abs/2205.03220v1
Date: Thu, 5 May 2022 12:07:00 GMT
Title: Internet of Things and Health Care in Pandemic COVID-19: System Requirements Evaluation
Authors: Hasan Naji, Nicolae Goga, Ammar Karkar, Iuliana Marin, Haider Abdullah Ali
Abstract summary: This paper aims to find the important requirements for a remote monitoring system for patients with COVID-19. The requirements and the value are determined for the proposed system, which integrates a smart bracelet that helps to signal patient vital signs.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Technology adoption in healthcare services has resulted in advancing care delivery services and improving the experiences of patients. This paper presents research that aims to find the important requirements for a remote monitoring system for patients with COVID-19. As this pandemic is growing more and more, there is a critical need for such systems. In this paper, the requirements and the value are determined for the proposed system, which integrates a smart bracelet that helps to signal patient vital signs. (376) participants completed the online quantitative survey. According to the study results, Most Healthcare Experts, (97.9%) stated that the automated wearable device is very useful, it plays an essential role in routine healthcare tasks (in early diagnosis, quarantine enforcement, and patient status monitoring), and it simplifies their routine healthcare activities. I addition, the main vital signs based on their expert opinion should include temperature (66% of participants) and oxygenation level (95% of participants). These findings are essential to any academic and industrial future efforts to develop these vital wearable systems. The future work will involve implementing the design based on the results of this study and use machine-learning algorithm to better detect the COVID-19 cases based on the monitoring of vital signs and symptoms.

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