Professor Abdoli-Eramaki explores wearable technology in early detection of COVID-19

“We hope to develop a technology to predict COVID-19 and similar infective diseases at their early stages among front-line employees,” said Mohammad  Abdoli-Eramaki, a professor at the School of Occupational and Public Health (SOPHe) at the Faculty of Community Services (FCS). “Human biomarkers are great indicators of when the body is affected. Wearable technologies such as wireless heart rate monitors can record heart rate conditions 24/7, and artificial intelligence (AI) algorithms can detect the biomarker variabilities.”

Abdoli-Eramaki is the principal researcher on a project entitled “A Remote Sensing Technology to Monitor COVID-19 Symptoms Using Artificial Intelligence.” This study addresses the need for monitoring early signs and symptoms of COVID-19, based on the World Health Organization (WHO) and the Public Health Agency of Canada’s best practice preventive guidelines. “The main aim of this project is to develop and validate a remote symptom monitoring system (RSMS) to continuously collect and analyze biomarkers of risk in order to track, detect and predict early signs and symptoms of COVID-19 using AI,” said Abdoli-Eramaki. “Health care professionals are a population that is both working in high-risk settings and where the earliest possible detection of infection can make a significant difference in curbing viral spread.” 

Through a combination of technologies, Abdoli-Eramaki’s research intends to facilitate quick screening and proper response so that the lives of health professionals are protected. He plans to use technologies including textile-based sensor devices, 5G communication channels and robust AI algorithms that will provide timely alarms to the target people as soon as COVID-19 symptoms are identified. “Myant Inc (external link) . has a newly developed sensor technology embedded in the fibre of underwear. Collaborating with them, we are going to develop a remote monitoring system to gather and analyze the biometric data, and detect the early-stage symptoms patterns of COVID-19.” Tracking and detecting early signs and symptoms have been a challenge and a priority throughout the pandemic. Abdoli-Eramaki’s project is different from others in that wearable technology combined with AI can passively monitor internationally agreed-upon risk factors like body temperature and respiratory and heart rates in higher-risk individuals 24/7 without interfering in their day-to-day activities. 

“Our approach is innovative in three ways,” said Abdoli-Eramaki. “First, it will allow us to monitor multiple signs simultaneously. Second, the integrated biometric information can be monitored in real-time for several days using multi-sensor devices so that higher accuracy could be achieved. Third, the interface is user-friendly, and the person can continuously monitor their own status using smartphones. We will complement our dataset with additional biomarkers, including the level of blood oxygen, and the sweating rate to explore the interrelation among biomarkers and adjust/calibrate our models. Wearable technology presents a remarkable opportunity to better understand disease prevention because it affords us more consistent, continuous and accurate measurements.” It is hoped that this could reduce strain on healthcare staff and facilities and reduce viral spread.

This project was funded by a $50,000 2020 Ryerson COVID-19 SRC Response Fund grant. Collaborators on this project include:

• Sri Krishnan, associate dean of research, Department of Engineering, Computer and Biomedical Engineering, Ryerson University
• Morteza Bashash (external link) , adjunct professor, School of Occupational and Public Health, Ryerson University and the Faculty of Medicine, University of Southern California
• Vahid Abolhasannejad (external link) , post-doctoral fellow, Transportation Engineering, Ryerson University
• Daniil Shuraev, undergraduate student, Electrical Engineering, Ryerson University
  
• Bavatharane Jeyanathan, undergraduate student, School of Occupational and Public Health, Ryerson University