Next Article in Journal
Reversible Room Temperature H2 Gas Sensing Based on Self-Assembled Cobalt Oxysulfide
Previous Article in Journal
Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor
Previous Article in Special Issue
Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification
Article

AI Based Monitoring of Different Risk Levels in COVID-19 Context

1
Engineering School, University of Minho, 4800-058 Guimarães, Portugal
2
Algoritmi Center, University of Minho, 4800-058 Guimarães, Portugal
3
2Ai, IPCA, School of Technology, 4750-810 Barcelos, Portugal
4
Polytechnic Institute of Cávado and Ave, 4750-810 Barcelos, Portugal
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Elisenda Bonet and Xavier Paolo Burgos-Artizzu
Sensors 2022, 22(1), 298; https://doi.org/10.3390/s22010298
Received: 2 November 2021 / Revised: 5 December 2021 / Accepted: 16 December 2021 / Published: 31 December 2021
(This article belongs to the Special Issue Recent Advances in Medical Image Processing Technologies)
COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people’s behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body temperature (using thermal cameras). For this task, synthetic data generation techniques were used to create hybrid datasets from public ones to train state-of-the-art algorithms, such as YOLOv5 object detector and a keypoint detector based on Resnet-50. For RGB mask detection, YOLOv5 achieved an average precision of 82.4%. For thermal masks, glasses, and caruncle detection, YOLOv5 and keypoint detector achieved an average precision of 96.65% and 78.7%, respectively. Moreover, RGB and thermal datasets were made publicly available. View Full-Text
Keywords: COVID-19; deep learning; supervised learning; object detection; keypoint detection COVID-19; deep learning; supervised learning; object detection; keypoint detection
Show Figures

Figure 1

MDPI and ACS Style

Melo, C.; Dixe, S.; Fonseca, J.C.; Moreira, A.H.J.; Borges, J. AI Based Monitoring of Different Risk Levels in COVID-19 Context. Sensors 2022, 22, 298. https://doi.org/10.3390/s22010298

AMA Style

Melo C, Dixe S, Fonseca JC, Moreira AHJ, Borges J. AI Based Monitoring of Different Risk Levels in COVID-19 Context. Sensors. 2022; 22(1):298. https://doi.org/10.3390/s22010298

Chicago/Turabian Style

Melo, César, Sandra Dixe, Jaime C. Fonseca, António H.J. Moreira, and João Borges. 2022. "AI Based Monitoring of Different Risk Levels in COVID-19 Context" Sensors 22, no. 1: 298. https://doi.org/10.3390/s22010298

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop