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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Amel Meddeb-Makhoulf
High School of Engineering in Electronics and Communications (ENET’COM)

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A smart emergency response system based on deep learning and Kalman filter: the case of COVID-19 Hounaida Frikha; Ferdaous Kamoun-Abid; Amel Meddeb-Makhoulf; Faouzi Zarai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp630-640

Abstract

During an epidemic, the transportation of patients to emergency departments and the monitoring of their physiological parameters pose significant challenges in this critical scenario. Swift and efficient diagnosis has the potential to rescue the lives of these patients. The objective is accomplished through the utilization of deep learning to categorize information into emergencies, prioritizing its dispatch. In this article, we present a sophisticated emergency system that employs deep learning to swiftly transmit vital information from emergency patients to the hospital that can provide the highest quality healthcare for these individuals. The fusion method integrates data obtained and refined from patients' electronic medical records with data acquired by the wireless medical sensor network during the transportation phase. Subsequently, the process of choosing the parameters is employed as inputs to the learning model. The data gathered and educational outcomes, such as emergency notifications, are transmitted through Wi-Fi and 5G devices in our sophisticated system. The proposed contribution achieves a 98% accuracy with a runtime of 1.53 seconds. This discovery demonstrates the efficacy of our system, particularly in the context of epidemic situations such as COVID-19.
Co-Authors Faouzi Zarai Ferdaous Kamoun-Abid Hounaida Frikha