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Secure Health Monitoring Communication Systems Based on IoT and Cloud Computing for Medical Emergency Applications.
Siam, Ali I; Almaiah, Mohammed Amin; Al-Zahrani, Ali; Elazm, Atef Abou; El Banby, Ghada M; El-Shafai, Walid; El-Samie, Fathi E Abd; El-Bahnasawy, Nirmeen A.
  • Siam AI; Department of Embedded Network Systems Technology, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt.
  • Almaiah MA; College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
  • Al-Zahrani A; College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
  • Elazm AA; Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • El Banby GM; Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • El-Shafai W; Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
  • El-Samie FEA; Security Engineering Laboratory, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia.
  • El-Bahnasawy NA; Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt.
Comput Intell Neurosci ; 2021: 8016525, 2021.
Article in English | MEDLINE | ID: covidwho-1598096
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 (SpO2), 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 SpO2, and 0.13, 0.11, and 0.003, respectively, for body temperature. These results demonstrate the high accuracy and reliability of the proposed system.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Cloud Computing / Internet of Things Limits: Humans Language: English Journal: Comput Intell Neurosci Journal subject: Medical Informatics / Neurology Year: 2021 Document Type: Article Affiliation country: 2021

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Cloud Computing / Internet of Things Limits: Humans Language: English Journal: Comput Intell Neurosci Journal subject: Medical Informatics / Neurology Year: 2021 Document Type: Article Affiliation country: 2021