Research Opportunities for HQPs

The PEAR lab has opening positions for HQPs at all levels, i.e., undergraduate, master's, Ph.D. and postdoctoral fellows. Interested students looking for a supervisor are welcome to contact the PEAR lab director directly.  

Project 1. DATA ANALYSIS TO IDENTIFY LOST PERSON PROFILES IN DEMENTIA RESEARCH

Student background: computer science and data science

Project summary: This project applies computer science and data science to pressing challenges in public health and dementia research. We will use real-world missing person datasets involving people living with dementia to conduct clustering analyses to determine lost person profiles and contribute to impactful research that bridges AI, data science, and healthcare. 

Project 2. DESIGN OF SECURE DATA INFRASTRUCTURES FOR DEMENTIA MISSING PERSONS RESEARCH

Student background: computer science and data science, with skills in cryptography, specifically in Fully Homomorphic Computing (FHE)

Project summary: This project investigates how Fully Homomorphic Encryption (FHE) can be applied to securely integrate and analyze multiple police datasets related to missing incidents involving individuals with and without dementia. The objective is to design privacy-preserving data infrastructures that enable cross-jurisdictional collaboration without exposing sensitive information. 

The student will develop and compare both centralized encrypted systems, where encrypted datasets are aggregated for secure computation, and federated encrypted systems, where analysis occurs locally and only encrypted outputs are shared. The research will include building encrypted data pipelines, implementing privacy-preserving record linkage where appropriate, and applying statistical or machine learning models directly on encrypted data.

The project will also evaluate computational performance, scalability, and governance considerations, including alignment with Canadian privacy legislation such as PIPEDA.

This interdisciplinary research sits at the intersection of cryptography, data science, and public safety analytics, contributing to proactive risk detection and improved prevention strategies in missing incidents research.

Project 3. DEVELOPMENT OF A ROBOTIC TOY FOR CHILDREN WITH DISABILITIES 

Student background: Mechanical or electrical engineering, with an emphasis on robotic and programming skills 

Project summary: This project will support several activities related to the development of a robotic toy enabling play activities of children with disabilities. This project aimed to develop two interactive games. These are the hide-and-seek and tag games. We will integrate hardware and software to develop these interactive games. In both games, the robot must be able to navigate in the house, locate and recognize the child. We will also develop a user interface to control the robot. This will include a special remote control to be used by children with movement limitations; and an Android-based app in order for parents, teachers, or therapists to set up the robot features (e.g., distance, rotation angles, sounds etc.) to conduct the therapeutic activities and games.    

Project 4. INTELLIGENT SENSING OF RANGE OF MOTION DURING PROGRESSIVE LIFTING IN RETURN-TO-WORK CAPACITY EVALUATION

Student background: Electrical engineering, with an emphasis on biomedical engineering and programming skills 

Project summary: This proof of concept and prototyping project is to enhance functional lift capacity testing with quantitative biomechanical data obtained from whole-body motion tracking, heart rate data, and machine learning, paired with a pilot study to begin to determine ROM normative ranges during functional lifting testing. We aim to further quantify and standardize this approach by using 3D motion tracking that can determine joint locations, range of motion, and movement patterns obtained from sensors placed across the body (i.e., XSens Awinda). Further, as heart rate is a component of the EPIC lifting capacity test, electrocardiogram monitoring will be included to the motion data.

Project 5. USE OF GUARDIO, A HEALTH CANADA-LICENSED MOBILE APPLICATION, AND MACHINE LEARNING TO DESCRIBE MOBILITY PATTERNS OF PERSONS LIVING WITH DEMENTIA 

Student background: Machine learning and computing science  

Project summary: The goal of this project is to examine the acceptance and usability of GuardIO - Family Care, mobile application. It supports persons with cognitive impairment and their care partners to develop risk mitigation strategies through understanding the patterns of their mobility by leveraging a cloud-based telematics platform licensed by Health Canada. This enables the care partners to receive timely care and support.  This Health Canada licensed app is developed by WeTraq and available on app stores and SunLife Lumino Health marketplace. It combines GPS and WiFi to provide real-time location monitoring and safety alerts. It does not require an additional device other than one’s personal smartphone. We will use machine learning driven analytics to describe mobility patterns of participants with dementia and without dementia (care partners). This information can be used to identify changes in mobility to inform decisions about personalized care and support services.  Increasing prevalence of dementia in Canada calls for strategies like GuardIO to address risks of getting lost and going missing, while supporting the health and wellbeing of persons aging in place.