Secure Health Monitoring Communication Systems Based on IoT and Cloud Computing for Medical Emergency Applications

TLDR

Smart health surveillance technology has attracted wide attention for early detection of critical abnormal situations without direct patient contact. This paper presents a secure, portable, IoT‑based multivital signal monitoring system. The system measures heart rate, blood oxygen saturation, and body temperature, encrypts the signals with AES, and transmits them via an ESP8266 Wi‑Fi module to cloud servers where they are decrypted and displayed, with performance compared to commercial devices. Results show the system’s measurements fall within a 95 % confidence interval, with RMSE, MAE, and MRE values of 1.44/1.12/0.012 for HR, 1.13/0.92/0.009 for SpO₂, and 0.13/0.11/0.003 for temperature, demonstrating high accuracy and reliability.

Abstract

Smart health surveillance technology has attracted wide attention between patients and professionals or specialists to provide early detection of critical abnormal situations without the need to be in direct contact with the patient. This paper presents a secure smart monitoring portable multivital signal system based on Internet‐of‐Things (IoT) technology. The implemented system is designed to measure the key health parameters: heart rate (HR), blood oxygen saturation (SpO 2 ), and body temperature, simultaneously. The captured physiological signals are processed and encrypted using the Advanced Encryption Standard (AES) algorithm before sending them to the cloud. An ESP8266 integrated unit is used for processing, encryption, and providing connectivity to the cloud over Wi‐Fi. On the other side, trusted medical organization servers receive and decrypt the measurements and display the values on the monitoring dashboard for the authorized specialists. The proposed system measurements are compared with a number of commercial medical devices. Results demonstrate that the measurements of the proposed system are within the 95% confidence interval. Moreover, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Error (MRE) for the proposed system are calculated as 1.44, 1.12, and 0.012, respectively, for HR, 1.13, 0.92, and 0.009, respectively, for SpO 2 , and 0.13, 0.11, and 0.003, respectively, for body temperature. These results demonstrate the high accuracy and reliability of the proposed system.