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IoMT-Based Healthcare Framework for Ambient Assisted Living Using a Convolutional Neural Network

Waleed T. Al-Sit1, Nidal A. Al-Dmour2, Taher M. Ghazal3,4,*, Ghassan F. Issa3

1 Department of Computer Engineering, Mutah University, Al-Karak, Jordan; Higher Colleges of Technology, Dubai, UAE
2 Department of Computer Engineering, College of Engineering, Mutah University, Jordan
3 School of Information Technology, Skyline University College, University City Sharjah, Sharjah, UAE
4 Center for Cyber Security, Faculty of Information Science and Technology, UKM, 43600, Bangi, Selangor, Malaysia

* Corresponding Author: Taher M. Ghazal. Email: email

Computers, Materials & Continua 2023, 74(3), 6867-6878. https://doi.org/10.32604/cmc.2023.034952

Abstract

In the age of universal computing, human life is becoming smarter owing to the recent developments in the Internet of Medical Things (IoMT), wearable sensors, and telecommunication innovations, which provide more effective and smarter healthcare facilities. IoMT has the potential to shape the future of clinical research in the healthcare sector. Wearable sensors, patients, healthcare providers, and caregivers can connect through an IoMT network using software, information, and communication technology. Ambient assisted living (AAL) allows the incorporation of emerging innovations into the routine life events of patients. Machine learning (ML) teaches machines to learn from human experiences and to use computer algorithms to “learn” information directly instead of relying on a model. As the sample size accessible for learning increases, the performance of the algorithms improves. This paper proposes a novel IoMT-enabled smart healthcare framework for AAL to monitor the physical actions of patients using a convolutional neural network (CNN) algorithm for fast analysis, improved decision-making, and enhanced treatment support. The simulation results showed that the prediction accuracy of the proposed framework is higher than those of previously published approaches.

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APA Style
Al-Sit, W.T., Al-Dmour, N.A., Ghazal, T.M., Issa, G.F. (2023). Iomt-based healthcare framework for ambient assisted living using a convolutional neural network. Computers, Materials & Continua, 74(3), 6867-6878. https://doi.org/10.32604/cmc.2023.034952
Vancouver Style
Al-Sit WT, Al-Dmour NA, Ghazal TM, Issa GF. Iomt-based healthcare framework for ambient assisted living using a convolutional neural network. Comput Mater Contin. 2023;74(3):6867-6878 https://doi.org/10.32604/cmc.2023.034952
IEEE Style
W.T. Al-Sit, N.A. Al-Dmour, T.M. Ghazal, and G.F. Issa "IoMT-Based Healthcare Framework for Ambient Assisted Living Using a Convolutional Neural Network," Comput. Mater. Contin., vol. 74, no. 3, pp. 6867-6878. 2023. https://doi.org/10.32604/cmc.2023.034952



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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