Spring, 2024
As she describes it, Rita Orji designs technology “to promote social good.”
Some of the applications the Dalhousie University computer science professor and Canada Research Chair and her students have produced promote physical health and wellness, while others seek to improve one’s mental health. Generally, these applications help people achieve desired changes in their behaviour, whether that be discouraging risky sexual practices or binge drinking, promoting physical activity and healthy diet, or improving mental health. Each application starts with Orji studying the people who might use them — often by collaborating with academics in other faculties — and then making users part of the design process.
“We design persuasive technologies,” Orji says, adding that they integrate modern technologies such as artificial intelligence and virtual reality into the apps they build to engage users and improve the apps’ effectiveness and user experience. “We need to keep the user in mind throughout the design process.”
One such app is a collaboration between Orji’s Persuasive Computing Lab in the faculty of computer science and the psychiatry department. Using information from a research study that aims to improve the mental well-being of youth, Orji and her colleague, Professor Sandra Meier, developed an app called PROSIT, which stands for “Predicting Risks and Outcomes of Social inTeractions.” The app passively collects information on the daily social interactions of youth, including how many calls they’re making, how many messages they’re sending and how often they’re using social media to interact with friends and family.
Another app Orji and her student, Oladapo Oyebode, developed is called TreeCare. The app simulates users’ physical activity to represent the growth of trees in a virtual garden. If they are physically active in the real world, their virtual trees will flourish. “If they are more sedentary or less physically active, their tree will start losing leaves, losing fruit and becoming unhealthy,” Oyebode says.
To be able to personalize the systems to individuals, Orji and her students collect behavioural and physiological data from their target audiences, and then analyze them to detect patterns or make predictions using AI techniques, including natural language processing and machine learning, all of which they do using the services of ACENET.
“For one project, we collected more than 47 million comments related to COVID-19,” Oyebode says. “We used ACENET’s resources to preprocess and analyze that data.” ACENET’s computing infrastructure helped the two to sift through the data, clean it up and prepare it for analysis, he says, adding that they performed keyphrase extraction and sentiment classification to uncover various issues related to the COVID-19 pandemic.
“ACENET has been really, really helpful for being able to crunch, process and analyze this big data,” Oyebode says.
Oyebode also added that they have used ACENET resources, such as the GPU-enabled clusters, to train advanced machine learning models that inform adaptive mental health interventions.
