FluNet: An AI-Enabled Influenza-Like Warning System
Loading...
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE sensors journal
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.
Description
Keywords
Cough detection, COVID, COVID-19, SARS, Face detection, Machine learning
Citation
Ward, R. J., Jjunju, F. P. M., Kabenge, I., Wanyenze, R., Griffith, E. J., Banadda, N., ... & Marshall, A. (2021). FluNet: An AI-Enabled Influenza-Like Warning System. IEEE sensors journal, 21(21), 24740-24748. https://doi.org/10.1109/JSEN.2021.3113467