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Grant: Alliance Grants

University of New Brunswick

$50,000.00

Automated detection and monitoring of health risks, such as COVID-19_x000d_ _x000d_

FY 2020-21

Summary
An emerging trend during the global COVID-19 pandemic is the adoption of new technologies by governments and businesses in order to track and mitigate the spread of the virus and future health risks. Industries throughout the world are being advised to implement active screening protocols to help protect their employees. New technologies must focus on automating and standardizing these screening procedures to help protect front-line workers and improve overall screening reliability. This project partners with Eigen Innovations, a Canadian company that specializes in integrating digitized thermographic data solutions into advanced industrial and commercial applications, to use an array of state-of-the-art imaging technologies to develop a connected tool for the automated detection and monitoring of health risks. The team comprises of a solutions engineer (PhD), a software architect and a product development leader from Eigen , and Dr. Dubay (PI) and Dr. Pickard (PDF) from UNB. Artificial intelligence research will be leveraged to analyze the collected image data to predict certain traits of an individual (e.g., age and gender), identify unique individuals by assigning virtual identification tags, and to detect and handle occluding objects in the images (e.g., a face mask or glasses). The availability of labelled data from various types of sensors, combined with trained learning models, will be utilized to improving screening reliability by checking multiple visible criteria, such as elevated body temperature, skin rash, and difficulty breathing. These trained models, as well as other considerations (e.g., sensor calibration, active black bodies, and external data sources), will be used to continuously improve the screening reliability by allowing adaptation of the unique flagging criteria associated with an individual. Furthermore, the proposed health monitoring tool will result in the creation of a Canada-based health database, and by utilizing Eigen's existing data collection and analytics platform, collaborating researchers and governments will be able to more easily track and study the spread of health risks.
Program
Alliance Grants
Location
New Brunswick, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
555209-2020
Name nom
Dubay, Rickey
Department département
Mechanical Engineering
Organization id
75
Institution établissement
University of New Brunswick
Province en
New Brunswick
Province fr
Nouveau-Brunswick
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2020
Award amount
50000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
518
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Medical equipment and apparatus
Area of application fr
Appareils et instruments médicaux
Research subject code
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Installment
inst:1 - 1
Partie
1
Nb partie
1
Application title
Automated detection and monitoring of health risks, such as COVID-19_x000d_ _x000d_
Keyword
NSERC
Application summary
An emerging trend during the global COVID-19 pandemic is the adoption of new technologies by governments and businesses in order to track and mitigate the spread of the virus and future health risks. Industries throughout the world are being advised to implement active screening protocols to help protect their employees. New technologies must focus on automating and standardizing these screening procedures to help protect front-line workers and improve overall screening reliability. This project partners with Eigen Innovations, a Canadian company that specializes in integrating digitized thermographic data solutions into advanced industrial and commercial applications, to use an array of state-of-the-art imaging technologies to develop a connected tool for the automated detection and monitoring of health risks. The team comprises of a solutions engineer (PhD), a software architect and a product development leader from Eigen , and Dr. Dubay (PI) and Dr. Pickard (PDF) from UNB. Artificial intelligence research will be leveraged to analyze the collected image data to predict certain traits of an individual (e.g., age and gender), identify unique individuals by assigning virtual identification tags, and to detect and handle occluding objects in the images (e.g., a face mask or glasses). The availability of labelled data from various types of sensors, combined with trained learning models, will be utilized to improving screening reliability by checking multiple visible criteria, such as elevated body temperature, skin rash, and difficulty breathing. These trained models, as well as other considerations (e.g., sensor calibration, active black bodies, and external data sources), will be used to continuously improve the screening reliability by allowing adaptation of the unique flagging criteria associated with an individual. Furthermore, the proposed health monitoring tool will result in the creation of a Canada-based health database, and by utilizing Eigen's existing data collection and analytics platform, collaborating researchers and governments will be able to more easily track and study the spread of health risks.