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Related Experiment Video

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FluNet: An AI-Enabled Influenza-Like Warning System.

Ryan J Ward1, Fred Paul Mark Jjunju1, Isa Kabenge2

  • 1Department of Electrical Engineering and ElectronicsUniversity of Liverpool Liverpool L69 7ZX U.K.

IEEE Sensors Journal
|May 18, 2022
PubMed
Summary

A new contactless device, FluNet, can detect symptomatic individuals by identifying coughs and monitoring temperature trends. This low-cost system offers a promising solution for influenza-like illness surveillance.

Keywords:
COVIDCOVID-19Cough detectionSARSface detectionmachine learning

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Area of Science:

  • Medical Technology
  • Artificial Intelligence
  • Public Health

Background:

  • Influenza poses significant global financial and resource challenges.
  • The COVID-19 pandemic highlights the need for effective, contactless health surveillance systems.
  • Existing surveillance methods may not be low-cost or readily deployable.

Purpose of the Study:

  • To develop FluNet, a novel, low-cost, contactless device for detecting symptomatic individuals.
  • To create a proof-of-concept system for identifying high-risk individuals for influenza-like illness.
  • To integrate face detection, temperature monitoring, and cough detection into a single surveillance solution.

Main Methods:

  • Utilized Long-Wave Infrared (LWIR) for contactless face detection with high precision (0.98) and recall (0.91).
  • Employed a deep convolutional neural network for real-time cough detection, achieving precision (0.95), recall (0.92), and AUC (0.98).
  • Integrated thermal imaging for temperature trend analysis (± 1 K accuracy) and directional cough detection (± 4.78° error).

Main Results:

  • FluNet demonstrated high accuracy in face detection and cough identification.
  • The system successfully integrated thermal sensing and cough analysis for symptomatic detection.
  • Developed two datasets for training and validating face detection and cough recognition models.

Conclusions:

  • FluNet represents a viable low-cost, contactless solution for influenza-like illness surveillance.
  • The developed system can aid in early detection and monitoring of symptomatic individuals.
  • Findings support the potential for edge computing applications in public health monitoring.