We live in a world where data is ubiquitous. From our smartphones and smartwatches to smart homes, our connected technology collects immense amounts of data with underexplored potential.
At the Ubiquitous Health Technology Lab (UbiLab), we drive innovation in health technology by applying artificial intelligence (AI), machine learning, and data engineering to strengthen health systems in Canada and around the world. Our interdisciplinary work spans public health and social media surveillance, climate and environmental health, mental health, infodemiology, human-computer interaction, and digital health policy. We are a multidisciplinary team of public health professionals, health scientists, AI researchers, data scientists, engineers, and designers working towards bridging the gap between health and technology. We collaborate with academic partners, governments, and global organizations to develop evidence-based guidelines, support technology transfer, and deliver scalable AI-driven solutions that advance public health resilience worldwide.
What we do:
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Health Information Trend Detection & Infodemiology
- Leveraging social media data to detect, monitor, and predict health-seeking behaviours and to map the spread of health misinformation online.
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Standards in Active Assisted Living (AAL) & AgeTech
- Developing guidelines and exploring smart, non-intrusive monitoring technologies to support independent aging and improve care.
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Big Data Infrastructure for Public Health
- Engineering robust data pipelines and ecosystems to analyze, integrate, and act on complex public health, climate and social media data.
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Sustainable Technology for Climate Change Adaptation
- Building health technology system frameworks for climate change adaptation, spanning both local and global contexts and enabling scalable solutions that are environmentally responsible and support health system resiliency.
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Mental Health Innovation
- Creating digital health solutions and data-driven tools to support mental health systems and respond to the growing global mental health crisis.
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Co-creating Systems for Public Health
- Applying systems thinking to co-design context-aware health technologies and supporting their implementation through knowledge and tech transfer.
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Generative AI in Public Health
- Applications of Generative AI in healthcare settings, specifically focusing on applying and integrating generative AI into Active Assisted Living (AAL) technologies and smart home environments.
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eHealth Technology Design
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Creating intelligent tools and platforms for both care providers and recipients to improve access, agency, and outcomes in health care.
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Developing privacy-preserving, radar-based technologies for continuous, non-contact health monitoring and early health intervention.
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This research focuses on developing privacy-preserving, radar-based monitoring systems for continuous, non-contact health assessment. Intelligent sensing technologies were designed to detect human activity, mobility, and vital signs in real-world environments without relying on cameras or wearable devices. The work enables early detection of health changes while maintaining user dignity, supporting aging-in-place and proactive healthcare strategies.
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We are looking for passionate students and early-career researchers interested in working for the future of healthcare to join our research team.
Research
We strive to leverage existing data sources to deliver pronounced benefits to both communities and individuals. Some of our projects include:
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Climate change healthy behaviour monitoring using Internet of Things (IoT)
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Guidelines for Active Assisted Living (AAL) and AgeTech in older adult care
UbiLab wins Healthy Behaviour Data Challenge
The Ubiquitous Health Technology Lab (UbiLab), headed by School of Public Health Sciences professor Plinio Morita, was one of three $25,000 winners in a national competition aimed at generating new methods to collect and use data in public health monitoring.
News
Featured on CTV: 'Life-or-death' issue: How one tool is identifying false health claims on social media
The sheer volume of social media posts published on a daily basis means health experts hoping to set the record straight face a near impossible challenge – how do you know which claims will sputter out, which ones will gain momentum, and the best way to counteract false messaging?
These are some of the questions researchers at the University of Waterloo in Ontario hope to answer with a tool used to identify health misinformation on social media. Named U-MAS, short for UbiLab Misinformation Analysis System, the University of Waterloo research tool is able to track health misinformation patterns before they become potential catastrophes.
Read the full article here.
Featured on Canadian Affairs: Self-serve machines fail to serve consumers with disabilities
Self-serve machines have become an ubiquitous way to place restaurant orders, buy transit tickets or purchase groceries. But for many consumers with disabilities, they are self-serve in name only. These machines often do not have features to make them accessible for consumers with disabilities, or consumers cannot find these features.
Few guidelines exist about how to make self-serve machines accessible to consumers with disabilities. A recent study from the University of Waterloo seeks to better understand the problems consumers with disabilities face when using self-serve machines, and how those barriers can be fixed.
Featured in the New York Post: How smart thermostats can reveal sleep patterns
Smart thermostats can provide insights into sleep quality, allowing researchers to infer a person’s sleep patterns without invasive monitoring. The finding is one of several developments presented this week at Sleep 2024, the annual meeting of the Associated Professional Sleep Societies.
Researchers analyzed eight terabytes of data collected from Ecobee smart thermostats in more than 178,000 households. Homeowners voluntarily contributed this data for energy efficiency research. The study team — led by postdoctoral researcher Jasleen Kaur — leveraged the Ecobee motion sensors to accurately identify complex sleep patterns and disturbances.