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Implementation of smart social distancing for COVID-19 based on deep learning algorithm.

Izaz Ul Haq1, Xianjun Du1, Haseeb Jan2

  • 1College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050 China.

Multimedia Tools and Applications
|April 25, 2022
PubMed
Summary
This summary is machine-generated.

Preventing viral spread involves social distancing. This study explores using deep learning for COVID-19 detection via cough analysis and smart tracking to enforce social distancing measures effectively.

Keywords:
Audio signalCovid-19Deep learningPandemicSocial distancing

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Area of Science:

  • * Computational epidemiology
  • * Public health informatics
  • * Artificial intelligence in medicine

Background:

  • * Viral diseases spread through respiratory droplets, necessitating social distancing.
  • * Airborne viral infections can alter voice characteristics, offering a potential detection vector.
  • * Early detection and isolation are crucial for controlling pandemics.

Purpose of the Study:

  • * To investigate methods for enabling, encouraging, and enforcing social distancing.
  • * To explore the use of deep learning for detecting individuals affected by viral diseases.
  • * To analyze AI-driven strategies for preventing disease transmission.

Main Methods:

  • * Voice feature analysis using deep learning for disease detection.
  • * Implementation of deep neural networks for identifying affected individuals.
  • * Scenario analysis involving COVID-19 detection via cough sounds.
  • * Integration of smart cameras and wireless technologies for tracking.
  • * Application of deep learning for disease spread prediction.

Main Results:

  • * Demonstrated the feasibility of detecting viral infections through voice analysis.
  • * Showcased the potential of deep learning models in identifying COVID-19 patients via cough.
  • * Validated the effectiveness of smart camera and wireless tracking for monitoring social distancing adherence.
  • * Identified AI-powered techniques as valuable tools for early pandemic intervention.

Conclusions:

  • * Deep learning offers a promising approach for early viral disease detection and tracking.
  • * AI-driven social distancing strategies can be implemented effectively during pandemics.
  • * Voice analysis and smart tracking technologies can enhance public health surveillance.
  • * These methods provide a scalable solution for smart social distancing implementation.