Ward, RJ, Jjunju, FPM, Kabenge, I, Wanyenze, R, Griffith, EJ, Banadda, N, Taylor, S and Marshall, A (2021) FluNet: An AI-Enabled Influenza-like Warning System. IEEE Sensors Journal, 21 (21). pp. 24740-24748. ISSN 1530-437X

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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. While 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; COVID-19; Cough detection; SARS; face detection; machine learning; 0205 Optical Physics; 0906 Electrical and Electronic Engineering; 0913 Mechanical Engineering; Analytical Chemistry
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions:	Computer Science & Mathematics
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
SWORD Depositor:	A Symplectic
Date Deposited:	29 Jun 2023 15:16
Last Modified:	29 Jun 2023 15:16
DOI or ID number:	10.1109/JSEN.2021.3113467
URI:	https://researchonline.ljmu.ac.uk/id/eprint/19615

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