Enhancing the Accuracy of Health Care Internet of Medical Things in Real Time using CNNets

Authors

  • muntadher Khamees 1Department of Computer Science, Science College, University of Diyala, Diyala, Iraq
  • Israa Mishkhal epartment of Computer Science, Science College, University of Diyala, Diyala, Iraq
  • Hassan Hadi Saleh Department of Computer Science, Faculty of physical education and sport silences, University of Diyala

DOI:

https://doi.org/10.24996/ijs.2021.62.11.34

Keywords:

Internet of Medical Things (IoMT), IoT, Deep learning, CNNets, Network sensors, RFID

Abstract

     This paper presents an efficient system using a deep learning algorithm that recognizes daily activities and investigates the worst falling cases to save elders during daily life. This system is a physical activity recognition system based on the Internet of Medical Things (IoMT) and uses convolutional neural networks (CNNets) that learn features and classifiers automatically. The test data include the elderly who live alone. The performance of CNNets is compared against that of state-of-the-art methods, such as activity windowing, fixed sample windowing, time-weighted windowing, mutual information windowing, dynamic windowing, fixed time windowing, sequence prediction algorithm, and conditional random fields. The results indicate that CNNets are competitive with state-of-the-art methods, exhibiting enhanced IoMT accuracy of 98.37%, which is the highest among the proposed solutions using the same dataset.

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Published

2021-11-30

How to Cite

Khamees, muntadher, Mishkhal, I. ., & Saleh, H. H. . (2021). Enhancing the Accuracy of Health Care Internet of Medical Things in Real Time using CNNets. Iraqi Journal of Science, 62(11), 4158–4170. https://doi.org/10.24996/ijs.2021.62.11.34

Issue

Section

Computer Science