Why the study?
Problems have arisen in diagnosing or predicting diseases due to human errors or lack of measurement accuracy, motivating an integrated health monitoring system to measure vital parameters and predict disease.
The proposed IoT-based health monitoring system achieved high accuracy in measuring vital parameters, though diagnostic accuracy for COVID-19 was moderate.
Proposed breath-based monitoring may support vital parameter tracking; leaves open clinical validation before any adoption.
The use of information technology and technological medical devices has contributed significantly to the transformation of healthcare. Despite that, many problems have arisen in diagnosing or predicting diseases, either as a result of human errors or lack of accuracy of measurements. Therefore, this paper aims to provide an integrated health monitoring system to measure vital parameters and diagnose or predict disease. Through this work, the percentage of various gases in the blood through breathing is determined, vital parameters are measured and their effect on feelings is analyzed. A supervised learning model is configured to predict and diagnose based on biometric measurements. All results were compared with the results of the Omron device as a reference device. The results proved that the proposed design overcame many problems as it contributed to expanding the database of vital parameters and providing analysis on the effect of emotions on vital indicators. The accuracy of the measurements also reached 98.8% and the accuracy of diagnosing COVID-19 was 64%. The work also presents a user interface model for clinicians as well as for smartphones using the Internet of things.
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Elgawad et al. (2022) studied this question.
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