- Current projects (see below)
- Student thesis projects
- Postdoctoral research fellowships
- Completed projects
A Big Data ecosystem for evaluating health misinformation on social media
Infodemics--Digital misinformation on social media has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. Consequently, there is a critical need for an expert system capable of processing vast amounts of health-related digital data to detect patterns of public health misinformation. To address this need, this research designed and developed the U-MAS, a big data pipeline and ecosystem for identifying and analyzing health misinformation on social media.
Leveraging AI to predict emotional states with UpBeing mobile data
In today’s fast-paced world, emotional well-being is increasingly recognized as a critical aspect of overall health. The increase in mental health issues highlights the need for innovative solutions to monitor and enhance emotional well-being. This research project addresses this need by using Artificial Intelligence (AI) to predict emotional states through the UpBeing data. UpBeing is designed to improve emotional intelligence and mental resilience, integrating passive behavioral data with science-based check-ins to show how daily activities affect mental health.
Leveraging AI and IoT to predict heatwaves in Canada: A climate health initiative
Extreme heat events in Canada pose a significant threat to public health, especially affecting vulnerable groups such as the elderly and individuals with pre-existing health conditions, who often spend considerable time indoors. Increasing indoor temperatures during heatwaves can worsen health problems and lead to heat-related illnesses. Accurate forecasting of indoor temperatures is thus crucial for informing public health strategies and mitigating the impacts of these extreme heat conditions. This project aims to address this challenge by predicting heatwaves by analyzing indoor temperature data collected from ecobee smart thermostats in North American households.
The use of IoT and big data by public and global health agencies to quantify adverse outcomes and health impacts of air pollution
Air pollution is a major global public health challenge responsible for numerous health issues in children and deteriorating environmental conditions with adverse outcomes on people’s health (Sofia, Gioiella, Lotrecchiano, & Giuliano, 2020). The purpose of this project is to create a real-time, crowdsourced, IoT-based air quality monitoring ecosystem using IoT sensors. In doing so, these data can then be used by the national public health agency to expedite the necessary steps to mitigate the adverse impacts of air pollution.
Dynamic consent interoperability resources
The COVID-19 pandemic demonstrated the need for rapid implementation of nation-wide public health interventions and access to personal data from the general population for academic and industry research. Personal data is any information collected from a data subject's devices (e.g., smartphones, sensors) that can reveal their identity. This research aims to create a Dynamic Consent Standard (DCS) describing data formats and elements (Resources), and an Application Programming Interface (API) for exchanging dynamic consent information between research stakeholders.
Mobile health platform for population-level health surveillance
In Public Health, surveillance is defined as the ongoing collection, analysis and dissemination of data to improve population health. Ultimately, this project will help to decrease costs and improve logistics, allowing researchers access to large and diverse participants. In addition, special attention will be paid to usability issues concerning older populations, so that they can benefit from the solution and be encouraged to keep track of their health, sustaining a healthy behaviour and lifestyle and gracefully age in place.
Dashboarding for complex datasets: a human-factors approach
Big data is changing the way data assessment is done. The challenges in dealing with complex datasets are present throughout the whole data pipeline, especially in the healthcare domain. Technical limitations (e.g. database structure, programming language, etc.), as well as ethical implications (e.g. privacy and consent), impose constraints, creating siloed databases, and, therefore, making it technically hard to integrate and combine information. Ultimately the main goal, from a healthcare standpoint, is to achieve a holistic understanding of the patients’ condition.
Public health surveillance of behavioral risk factors for chronic diseases in Canada using big data from internet of things and artificial intelligence
Public health surveillance has developed in recent years as technology has progressed to deliver the requirements of such a system. However, there is still room for innovation in the types of technologies that are developed, used, and implemented. The solutions provided in this study can expand beyond typically defined features and be used for more holistic health monitoring purposes at population level.
Climate change healthy behaviour monitoring using Internet of Things (IOT)
Climate change is the biggest threat to humans in the 21st century, and with global warming already occurring, we must prepare to live in a warmer climate that will experience more frequent, intense and longer extreme heat events (Patz et al., 2005)(Costello et al., 2009). The UbiLab has a research partnership with ecobee, a Canadian smart thermostat company, and access to over 110,000 household temperature records from across North America. The use of this unobtrusive, commercially available monitoring system in Canadian homes would overcome barriers such as cost, and enable long periods of data collection that could save lives.
Scaling mHealth and wearable technology in Canada
Scaling mHealth and Wearable Technology within the Canadian public healthcare system is a very complex issue that involves various stakeholders. There are certain gaps associated with the perception of risk when it comes to ideating, prototyping, deploying certain healthcare focused technologies. Academic research on commercialization factors behind mHealth and the use of wearable technology is relatively a new field, and there is significance behind defining and further understanding this part of the healthcare system as this is where the future of healthcare is going to head towards.
Designing contact tracing apps as persuasive technologies to make them more effective
The adoption of current contact tracing apps (aka, exposure notification apps) aimed to combat COVID-19 has been low and slow. The research investigates the potential effectiveness of leveraging persuasive strategies to motivate users to carry out specific behaviors or actions that are vital to curbing the spread of the coronavirus, e.g., downloading, installing and using the app, self-isolation if users receive an exposure alert, report of COVID-19 diagnosis if users test positive.
Thinktum
Social isolation is having a significant impact on the quality of life, physical activity, and sleep patterns of our population. While self-isolation and social distancing provide the most successful method for limiting the progression and spread of infectious diseases like COVID-19, we often overlook the impact of these rules on our population. In this 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.
Accelerating virtual care through COVID-19
This research study aims to identify opportunities to bridge the gap between underutilized virtual care services, through the acceleration of the COVID-19 pandemic, and develop recommendations for how the use of virtual care services can effectively be sustained.
Workers’ health and safety real-time monitoring using wearable technology to enhance construction management practices
With current advances in wearable devices and optimization technologies, this project aims to provide a low-cost system for automated monitoring of workers’ exposure to hazards and support better decisions for schedule fast-tracking and recovery. This should greatly help construction companies meet their objectives without overstressing construction workers. The project is funded by UW Interdisciplinary Trailblazer Fund.
Blockchain platform for consent management in healthcare
Introducing blockchain into the consent process can improve the safety, security and privacy of research stakeholders in healthcare studies. Blockchain can provide an immutable and timestamped log of consent, making the process more transparent for everyone involved.
Public health surveillance of behavioural risk factors in Canada using big data from internet of things and artificial intelligence
Public health surveillance has developed in recent years as technology has progressed to deliver the requirements of such a system. However, there is still room for innovation in the types of technologies that are developed, used, and implemented. The solutions provided in this study can expand beyond typically defined features and be used for more holistic health monitoring purposes at the population level.
Privacy and trust in healthcare IoT data sharing: A snapshot of users' perspectives
This study will review the existing literature to understand the main concerns related to data sharing and what leads an individual to trust or not trust the organization in the process of sharing their personal health data. Along with the literature review, three surveys/online studies will be used to understand user concerns and fears when sharing their data through IoT devices or mobile apps.
Data visualization for Precision medicine
Technology is driving medicine towards a new era where new devices and large amounts of data come together to play an important role on diagnosis and treatments. The objective of this research is to design an ontological database of data visualization for precision medicine.
Comparing accuracy of clinically approved trackers against consumer-level wearables
Increasing physical activities can help maintain and improve health, including decreased risks of chronic disease mortality. This project will compare 3 different clinically approved trackers (Omron Pedometer HJ720IT, Piezo RxD, and ActiGraph GT9X Link) against each commercially available digital tracker, which consists of different versions of Apple Watches, Fitbits, and Xiaomi Mi Bands. The goal of this project is to determine if commercially available digital trackers are as accurate as clinically approved trackers in counting steps and, in turn, measuring physical activities.
Smart predictive analytics for Hill-Rom's data ecosystems
A pioneer in medical technology, Hillrom specializes in patient diagnostics and recovery and has designed the Centrella Smart+ bed. The bed is equipped with a suite of sensors (pressure, monitor, etc.) and data pertaining to healthcare delivery and patient safety. The overall objective of our current partnership with Hillrom is to improve the capabilities of the Centrella Smart+ bed to increase patient safety by leveraging its current suite of sensors.
Ambient physiology and activity monitoring
Capturing and interpreting people’s activity and vital signs is central to monitoring and managing their health. We are creating new and innovative ways of embedding sensors and systems into people’s environments to enable zero-effort remote monitoring. This project includes cutting-edge research into new wireless and computer vision sensors, embedding sensors into everyday objects, machine learning for activity recognition and data fusion, and interface development to effectively relay information to a diversity of stakeholders.
Remote patient monitoring using smart thermostat data
This study aims to analyze remote sensor records using machine learning and statistical methods to extract meaningful healthcare insights at individual level patient monitoring. It starts with the hypothesis that with this kind of record for aging patients for example, anomalies to their patterns and behaviours could help identify opportunities for proactive care, including family alerts.
Usability of mHealth applications in immigrant populations
This project focuses on improving the usability of diabetes self-management applications for immigrant populations. This project uses the diabetes mobile application, bant II, as a primary testbed to identify the barriers preventing immigrants from using these self-management applications. Using this data, the project aims to improve the cross-cultural usability of diabetes self-management applications and inform future mHealth application design for immigrant communities.
Commercialization pathways for mHealth technology
UbiLab is currently working on a scoping review combined with interviews to understand the commercialization landscape relevant to eHealth and mHealth use in Canada. In the recent few years, eHealth and mHealth focused apps have been growing at a significant rate and that has bought in unique set of challenges in a knowledge-driven economy like Canada. This study will help to further identify those challenges and the kind of opportunities can come out of it from the discussion.
IoT + mHealth data integration framework
UbiLab is currently working on a study to understand what Active/Ambient Assisted Living (AAL) or IoT for Healthcare technology companies are using as policy guidelines and standards in the creation of their products and services. Ultimately, this study will highlight the gap between what is currently available for innovators in terms of data security, privacy and encryption, and what should be developed to ensure that AAL technology is designed ensuring benefits to patients and the healthcare system through safe technologies.
Rapid population level surveillance of PASS indicators using ecobee's smart thermostat
The UbiLab is working with ecobee, a Canadian manufacturer of smart thermostats that uses wireless sensors (temperature, presence/movement) to adjust the temperature in the home according to usage and consequently, save energy. This study leverages data collected through ecobee’s “Donate Your Data" Program. We are expanding ecobee's research commitments into the healthcare domain to identify the amount of physical activity, sleep patterns and sedentary behaviour, particularly in older adults, using the sensor data generated.
Developing apnea detection algorithms for Studio 1 Labs SmartBedsheet
The UbiLab is supporting Studio1Labs in the design and evaluation of a vital signs and sleep monitoring technology using their proprietary smart fabric technology. This project aims to validate and test the novel functional bedsheet through a clinical study to collect data for assessment, in comparison to various clinically validated sensors (Hexoskin and pulse oximeter).
Real-time analytics platform
Through a collaboration with the Centre for Global eHealth Innovation at the University Health Network (UHN) with Dr. Joseph Cafazzo, we are developing a platform that can deliver real-time insights on the usage of the mHealth and eHealth technology, in combination with associated health outcomes.
The future of AAL: Exploring the continuum between AAL technologies, AAL services, and smart communities
This project is a collaboration between the University of Waterloo and the CSA Group with the objective of better understanding the requirements of AAL and smart technology use in the care of older adults. We are exploring the continuum connecting AAL technologies, healthcare service provision, and smart communities by researching existing work, conducting interviews with stakeholders in older adult care and AAL technology development, and collaborating with industry and research partners. With the help of these findings, we will provide guidelines, checklists, and propose new standards for current and future smart communities seeking to implement AAL technologies at a community level.
Support Healthy Aging with Smart Home Technology: A data ecosystem for Elderly Healthcare Monitoring
This project is a collabration between researchers from the Research Institute for Aging in Waterloo, two smart home technology companies (Swidget and SmartONE), with the aim of developing a smart home based data ecosystem to support independent living for older adults in Canada. In this project, we propose the development of an ecosystem to aggregate data from existing smart home technologies coupled with machine learning algorithms to detect anomalies in everyday behaviours. A remote monitoring platform would notify caregivers or family members when an expected behaviour is not observed, or a sensor reading is out of normal bounds. This project will enable caregivers and care providers to use smart home sensor data to monitor family members living
independently.