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FluNet: An AI-Enabled Influenza-like Warning System
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  • Ryan Ward ,
  • Fred Jjunju ,
  • Elias Griffith ,
  • Alan Marshall ,
  • Stephen Taylor ,
  • Isa Kabenge ,
  • Noble Banadda ,
  • Rhoda Wanyenze
Ryan Ward
University of Liverpool

Corresponding Author:[email protected]

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Fred Jjunju
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Elias Griffith
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Alan Marshall
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Stephen Taylor
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Isa Kabenge
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Noble Banadda
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Rhoda Wanyenze
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Influenza, an acute viral respiratory disease that is currently causing severe financial and resource strains worldwide. With the recent COVID-19 pandemic exceeding 153 million cases worldwide, there is a need for a low-cost and contactless surveillance system to detect symptomatic individuals, more so in counties with limited healthcare resources. As with many diseases, there are bio-clinical signals relating to the physical symptoms. The main objective of this study was to develop FluNet, a novel, proof-of-concept, low-cost and contactless device for the detection of high-risk individuals. The system passively conducts face detection in the longwave infrared domain with a precision rating of 0.9798 and mean intersection over union of 0.7386 while sequentially taking the temperature trend of faces with a thermal accuracy of ± 1 K. While in parallel determining if someone in audible proximity is coughing by using a custom deep convolutional neural network with a precision rating of 0.9519. In addition to presenting FluNet, two datasets have been constructed, one for face detection in the longwave infrared domain consisting of 250 images of 20 participants’ faces at various rotations and coverings, including face masks. The other for the real-time detection of cough patterns comprised of a sizeable dataset of 40,482 cough / not cough sounds, coupled with a new lightweight artificial neural network architecture for the classification of cough spectrograms. These findings could be helpful for future low-cost edge computing applications for influenza-like monitoring.
01 Nov 2021Published in IEEE Sensors Journal volume 21 issue 21 on pages 24740-24748. 10.1109/JSEN.2021.3113467