Use of AI and Wearable Data for Public Health Monitoring

In this proposed project, our research team aims to develop a data ecosystem to use consumer-level technologies such as fitness trackers and wearables to support public health decision making. We will use the COVID-19 pandemic as a testing scenario for the technology, exploring the data to improve our understanding of the impact that social-isolation had on: (1) population levels of physical activity and sleep, (2) quality of the work experience for employees working from home and (3) stress level. Through the use of data mining systems previously developed at the UbiLab, in addition to new ones developed under the umbrella of this MITACS Accelerate project, we will monitor the impact of quarantine rules on household-level and individual-level physical activity (e.g., duration, type, intensity), sleep quality (e.g., duration and disturbed sleep patterns), mental health (through stress level estimation).

Faculty Supervisor:

Plinio Pelegrini Morita

Student:

Partner:

Université de Technologie de Compiègne

Discipline:

Life Sciences

Sector:

Education

University:

University of Waterloo

Program: