Grant: Alliance Grants
Ryerson University
$49,900.00
A Thermal-Optical Vision System for Identification and Localization of Symptomatic COVID-19 Patients_x000d_
EF 2020-21
Résumé
According to World Health Organization (WHO) officials, timely identification and isolation of individuals infected with coronavirus is the key to containing the spread of the virus. With a shortage of testing kits, laboratory supplies, and personnel, temperature screening remains the primary approach to identifying high risk individuals. Thermal screening is non-invasive and gives immediate results for one of the primary symptoms of COVID-19. However, current methods of COVID-19 public screening in 'high traffic' environments involve handheld temperature measurements which are considered inefficient, ineffective, and potentially risky to the screener and other acutely ill patients. Relying on only elevated skin temperatures (and not core temperatures) using distance measurement units might also give false positive classification results. It is hypothesized that fusing temperature measurements with body gait patterns (markers) would increase the accuracy of identifying symptomatic COVID -19 patients.
In this project, researchers from Ryerson University are partnering with Altius Analytics Labs to enhance the scalability and efficiency of COVID-19 public screening and disease tracking. The main goal of the project is to develop, validate, and test a novel thermal-optical vision-based system to automatically identify symptomatic individuals on a large scale and track their movement through an environment. The outcome of this project will have a significant impact, protecting public health for the current pandemic and providing safeguards against a potential second wave. In addition to medical screening applications, results can be extended to the human factors industry as well. Finally, the project includes significant multidisciplinary training of highly qualified personnel (HQP) in established and emerging areas, including kinesiology, instrumentation, machine vision, signal processing, and machine learning. HQP will also have opportunities to work with the project partner and gain career relevant industrial experience.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
554496-2020
Name nom
Janabi-Sharifi, Farrokh
Department département
Mechanical and Industrial Engineering
Organization id
43
Institution établissement
Ryerson University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2020
Award amount
49900
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
1101
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Human health (including medically-related psychological research)
Area of application fr
Santé publique (y compris recherche psychologique liée à la médecine)
Research subject code
1901
Research subject group en
Biomedical engineering
Research subject group fr
Génie biomédical
Research subject en
Biomedical technology
Research subject fr
Technologie biomédicale
Installment
inst:1 - 1
Partie
1
Nb partie
1
Application title
A Thermal-Optical Vision System for Identification and Localization of Symptomatic COVID-19 Patients_x000d_
Keyword
NSERC
Application summary
According to World Health Organization (WHO) officials, timely identification and isolation of individuals infected with coronavirus is the key to containing the spread of the virus. With a shortage of testing kits, laboratory supplies, and personnel, temperature screening remains the primary approach to identifying high risk individuals. Thermal screening is non-invasive and gives immediate results for one of the primary symptoms of COVID-19. However, current methods of COVID-19 public screening in 'high traffic' environments involve handheld temperature measurements which are considered inefficient, ineffective, and potentially risky to the screener and other acutely ill patients. Relying on only elevated skin temperatures (and not core temperatures) using distance measurement units might also give false positive classification results. It is hypothesized that fusing temperature measurements with body gait patterns (markers) would increase the accuracy of identifying symptomatic COVID -19 patients.
In this project, researchers from Ryerson University are partnering with Altius Analytics Labs to enhance the scalability and efficiency of COVID-19 public screening and disease tracking. The main goal of the project is to develop, validate, and test a novel thermal-optical vision-based system to automatically identify symptomatic individuals on a large scale and track their movement through an environment. The outcome of this project will have a significant impact, protecting public health for the current pandemic and providing safeguards against a potential second wave. In addition to medical screening applications, results can be extended to the human factors industry as well. Finally, the project includes significant multidisciplinary training of highly qualified personnel (HQP) in established and emerging areas, including kinesiology, instrumentation, machine vision, signal processing, and machine learning. HQP will also have opportunities to work with the project partner and gain career relevant industrial experience.
