Developing an intelligent system for monitoring vital signs using wearable medical sensors

Abstract

This study aims to develop an intelligent system for monitoring patients' vital signs using a set of wearable medical sensors. It leverages artificial intelligence techniques and neural networks capable of continuously analyzing physiological data to facilitate the early detection of dangerous health changes before disease progression. The proposed system collects vital patient data such as heart rate, body temperature, blood oxygen level, and respiratory rate via the wearable sensors. This data is then transmitted to an analysis unit that utilizes neural network models to analyze vital patterns and detect abnormal indicators. The study employed a descriptive, analytical, and applied methodology to design and evaluate the proposed system. The model was tested on a set of vital data collected from users under various monitoring conditions. Performance was analyzed using several statistical evaluation indicators, including accuracy, sensitivity, and abnormality detection rate. The results indicated that the proposed system contributed to improving patients' quality of life index by 18% to 34% due to its ability to continuously monitor health and provide early warning of abnormalities. The results also showed that the time required to detect critical health changes decreased by 32% to 47% compared to traditional monitoring methods that rely on periodic measurements. Furthermore, the proposed model achieved an accuracy of approximately 96.8% in classifying normal and abnormal conditions, exceeding the accuracy of many previous studies, whose models ranged from 88% to 93%. These results indicate the effectiveness of the proposed smart system in improving vital sign monitoring and supporting early detection of health changes. They also confirm the potential of employing wearable medical sensors and artificial intelligence technologies in developing smart health systems that support remote healthcare and contribute to improving patients' quality of life.