FluNet: An AI-Enabled Influenza-like Warning System



Ward, Ryan J ORCID: 0000-0002-9850-5191, Jjunju, Fred Paul Mark ORCID: 0000-0001-6257-434X, Kabenge, Isa, Wanyenze, Rhoda, Griffith, Elias J, Banadda, Noble, Taylor, Stephen ORCID: 0000-0002-2144-8459 and Marshall, Alan ORCID: 0000-0002-8058-5242
(2021) FluNet: An AI-Enabled Influenza-like Warning System. IEEE Sensors Journal, 21 (21). p. 1.

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Abstract

Influenza is an acute viral respiratory disease that is currently causing severe financial and resource strains worldwide. With the COVID-19 pandemic exceeding 153 million cases worldwide, there is a need for a low-cost and contactless surveillance system to detect symptomatic individuals. The 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 conducts face detection in the LWIR with a precision rating of 0.98, a recall of 0.91, an F-score of 0.96, and a mean intersection over union of 0.74 while sequentially taking the temperature trend of faces with a thermal accuracy of ± 1 K. In parallel, determining if someone is coughing by using a custom lightweight deep convolutional neural network with a precision rating of 0.95, a recall of 0.92, an F-score of 0.94 and an AUC of 0.98. We concluded this study by testing the accuracy of the direction of arrival estimation for the cough detection revealing an error of ± 4.78°. If a subject is symptomatic, a photo is taken with a specified region of interest using a visible light camera. Two datasets have been constructed, one for face detection in the LWIR consisting of 250 images of 20 participants' faces at various rotations and coverings, including face masks. The other for the real-time detection of coughs comprised of 40,482 cough / not cough sounds. These findings could be helpful for future low-cost edge computing applications for influenza-like monitoring.

Item Type: Article
Uncontrolled Keywords: COVID-19, Cameras, Temperature measurement, Sensors, Temperature sensors, Artificial intelligence, Pandemics, Cough detection, COVID, COVID-19, SARS, face detection, machine learning
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 04 Oct 2021 10:00
Last Modified: 18 Jan 2023 21:27
DOI: 10.1109/jsen.2021.3113467
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3139240