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Go to Editorial ManagerThis work concerns creating a monitoring system for a smart hospital using Raspberry Pi to measure vital signs. The readings are continually sent to central monitoring units outside the room instead of being beside the patients, to ensure less contacting between the medical staff and patients, also the cloud is used for those who leave the hospital, as the design can track on their medical cases. Data presentation and analysis were accomplished by the LabVIEW program. A Graphical User Interface (GUI) has been created by the Virtual-Instrument (VI) of this program that offer real-time access to monitor patients’ measurements. If unhealthy states are detected, the design triggers alerts and sends SMS message to the doctor. Furthermore, the clinicians can scan a QR code (which is assigned to each patient individually) to access its real-time measurements. The system also utilizes Electrocardiography (ECG) to detect abnormalities and identify specific heart diseases based on its extracted parameters to encourage patients to seek timely medical attention, while aiding doctors in making well-informed decisions. To evaluate the system’s performance, it is tested in the hospital on many patients of different ages and diseases as well. According to the results, the accuracy measurement of SpO2 was about 98.39%, 97.7% for (heart rate) and 98.7% for body temperature. This shows that the system can offer many patients receiving health services from various facilities, and it ensures efficient data management, access control, real-time monitoring, and secure patient information aligning with healthcare standards.
The advancement of pressure sensors customized for purposes marks notable progress, in healthcare diagnostics and patient supervision. This article delves into creating and assessing of a capacitive pressure sensor designed to measure physiological pressures with utmost accuracy and sensitivity. The sensor’s structure integrates materials compatible with the body to ensure safety and dependability when interacting with bodily tissues. Thorough simulations and validations showcase the sensors performance emphasizing its responsiveness across various pressures in medical settings. The assessment encompasses an analysis of the sensor’s sensitivity at (12.4 fF/mmHg) exceptional linearity within a nonlinearity range of ±0.015% with a small diaphragm diameter (0.5 mm) and long-term reliability. The results indicate that the suggested capacitive pressure sensor exhibits promising possibilities for use in fields like blood pressure monitoring, intracranial pressure measurement and other crucial areas of biomedicine, providing a nonintrusive and cost-efficient method, for real-time health monitoring and diagnostic purposes.