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Detection and Spatial Correlation Analysis of Infectious Diseases Using Wireless Body Area Network Under Imperfect

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This study introduces a deep learning model for COVID-19 detection using wireless body area networks (WBANs). The algorithm effectively monitors patients in real-time, even with imperfect communication channels between sensors and the central unit.

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Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Artificial Intelligence

Background:

  • Wireless Body Area Networks (WBANs) utilize biosensors to monitor physiological parameters like temperature and ECG.
  • Deep learning applied to WBAN data aids in disease monitoring, including respiratory issues and fever.
  • Communication channels in WBANs are prone to fading and imperfections due to body and environmental factors.

Purpose of the Study:

  • To propose a deep learning-based COVID-19 detection scheme within a WBAN framework.
  • To evaluate the performance of the proposed scheme under imperfect channel conditions.
  • To analyze the impact of correlation on WBAN performance with imperfect channels.

Main Methods:

  • Development of a deep learning algorithm for COVID-19 detection.
  • Implementation of the algorithm within a simulated WBAN setup.
  • Analysis of channel correlation effects on WBAN communication.

Main Results:

  • The proposed deep learning algorithm demonstrates effective COVID-19 detection.
  • Promising results were achieved for real-time patient monitoring.
  • The study analyzed the influence of imperfect channels and correlation on WBAN performance.

Conclusions:

  • Deep learning in WBANs offers a viable approach for real-time COVID-19 monitoring.
  • The proposed scheme is robust even in the presence of imperfect communication channels.
  • Further analysis of channel characteristics is crucial for optimizing WBAN performance.