3 Mitacs Globalink (GRI) research projects for Summer 2027.
1. Influence of awareness and personality traits on balance control with and without external perturbations
Experiencing and correcting errors during practice is important for acquiring motor skills, including improving balance control. However, emerging evidence suggests that some people are hyper-vigilant to small errors in balance control, which is associated with maladaptive behaviours. It is not known how vigilance influences error-augmented motor learning. This study aims to determine how error awareness and personality traits (locus of control, and attention to movement/motor error) influence balance control with and without external perturbations. Healthy young adults will complete a stabilometer balance task under 5 conditions (3 40-second trials per condition): unperturbed-aware, perturbed-unaware, perturbed-aware, unperturbed-unknown, and perturbed-unknown. For aware and unaware conditions, participants will be explicitly told there will or will not be external perturbations. In the unknown conditions, participants will be told that there may be external perturbations. The trial order will be counter-balanced across participants to control for order effects. Participants will complete Rotter’s Locus of Control scale (RLOC), the Movement-Specific Re-investment Scale (MSRS), and the Balance Vigilance Questionnaire (BVQ). We will test hypotheses exploring interactions between personality traits and presence/awareness of perturbations, and balance outcomes: 1) there will therefore be a significant interaction for all outcomes between RLOC and awareness of perturbations (i.e., aware versus unknown) in the unperturbed conditions as people with external locus of control will be more likely to believe that external perturbations are present in the unknown conditions; 2) sample entropy will be lower for those with high attention to movement (BVQ and MSRS) compared to those with low attention to movement across all conditions; and 3) there will be a greater decrease in sample entropy for those with low attention to movement, compared to those with high attention, in the perturbed – aware condition compared to the perturbed – unaware condition.
Research area, student roles & skills
Research area: We conduct research to improve safe independent mobility for older adults and people with stroke. Our research spans basic science (e.g., practice-based motor adaptation), to clinical trials aimed at increasing physical activity and preventing falls, and implementation research to move effective interventions into practice.
Student roles:
Under the guidance of the supervisor, working closely with a graduate student, and with the help of a research assistant, the student will assist with conduct of the study, including recruiting human participants, collecting and analysing the data, and presenting the results. The student will learn to use a custom motion platform, force plates and 3D motion capture. They may also learn to write custom code (using software such as Matlab and Stata) for processing and analysing the data. They will improve their written and oral communication skills by writing a final report and presenting the findings to our lab group. They will also have the opportunity to expand their knowledge and skills through involvement in our team's research projects and attending workshops/lectures.
Skills required:
Candidates should be enthusiastic, eager to learn, flexible, and comfortable working with human research participants. This position would suit a student interested in pursuing a career in health research, healthcare, rehabilitation sciences, or physiotherapy. We are looking for someone who is both a team player and who is also self-directed to work independently. Some knowledge of exercise and sensorimotor physiology, statistics and research design would be as asset. Basic skills in exercise testing and understanding of electromyography, kinematics, kinetics, and motion analysis would also be beneficial.
2. SportVision: Analyzing Sports Skills and Strategies with Computer Vision
This research project aims to leverage artificial intelligence (AI) and computer vision to revolutionize the analysis of hockey games. By using video recordings of hockey matches, the project seeks to develop a comprehensive system that can analyze player movements, evaluate team strategies, and assess injury risks.
The project has four key objectives:
Player Skill Evaluation: The system will extract data on individual player movements and actions, allowing for the quantification of their skills. This includes tracking skating speed, puck handling, and shot accuracy, providing insights into individual performance and growth.
Team Strategy Analysis: By examining the interplay between players, the system aims to identify and evaluate team strategies. This involves recognizing formations, passing patterns, and zone coverage, which will help coaches and analysts understand how teams function as a unit and adapt their tactics accordingly.
Performance Metrics: The system will generate quantitative metrics to compare the performance of different teams. These metrics will include possession time, successful passes, and scoring efficiency, offering a comprehensive overview of each team's strengths and weaknesses.
Injury Risk Assessment: The system will monitor player interactions to identify events that could lead to injuries, such as collisions and falls. This will help assess the risk of injury and provide valuable data for developing safer gameplay strategies.
This project will contribute to the advancement of sports analytics by providing a powerful tool for evaluating player skills, team dynamics, and game safety. The outcomes will benefit players, coaches, analysts, and sports enthusiasts alike.
Research area, student roles & skills
Research area: At IDEA Lab (see our website at: goidealab.com) at the University of Alberta, we are focused on developing autonomous intelligent systems to deliver personalized health, specially using wearable, artificial intelligence, and robotic systems. Using these technologies, we measure movement and physiological data. With cost-effective robotic systems, we can perform assessment of human function and deliver therapy. Commonly, we combine these with artificial intelligence (e.g., deep learning) and biomedical signal processing.
Student roles:
The student's role in this project includes:
Data Collection: The student will gather and preprocess video recordings of hockey matches, ensuring the data is suitable for analysis.
Algorithm Development: The student will work on developing and fine-tuning algorithms for player tracking, movement analysis, and team strategy recognition. This includes implementing AI models to extract meaningful insights from the video data.
Metric Generation: The student will design methods to generate quantitative metrics that can be used to compare individual players and teams, incorporating statistical techniques for analysis.
Injury Assessment: The student will implement mechanisms to detect and analyze events that may pose injury risks to players, collaborating with domain experts to ensure the validity of the assessment.
Collaboration: The student will work closely with a multidisciplinary team of graduate students to integrate domain knowledge into the system, making it relevant and useful for real-world applications.
Reporting: The student will document the progress of the project, including methodologies, findings, and conclusions, and will present these in meetings with supervisors and stakeholders.
Skills required:
The ideal student for this project should have:
A background in computer science or engineering, with coursework in AI and computer vision.
Proficiency in programming languages such as Python.
Familiarity with machine learning frameworks like TensorFlow or PyTorch.
An understanding of sports analytics and data analysis techniques as an ASSET.
3. Vélos intrumentés dans les milieux de travail (instrumented bike in the workplace)
The role of the intern will be to contribute to the various research protocols in place within the laboratory relating to the application of industry 4.0 in health-safety-environment to various clienteles of workers. More precisely, he will work on the development and implementation of different research and instrumentation protocols for workers in order to carry out real or delayed assessments of the workload and the risks to which workers are exposed in the workplace. framework of their roles and functions. More precisely, he will work using data already collected or to be collected to develop different signal processing algorithms in order to characterize the type of work carried out and calculate the demand of the latter on the worker. The workers with whom the intern will work are police officers patrolling by bicycle. Everything is carried out with the aim of an integrated sensor platform making it possible to quantify both the physiological load of the job on the worker by "clothed" sensors and by the instrumentation of the bicycle with regard to external variables such as atmospheric and road environment.
Research area, student roles & skills
Research area: My research expertise focuses on movement analysis and motor learning and control. More specifically, I am interested in how we can assess and train people to drive more safely using different interventions. Everything is addressed using techniques drawn from human factors assessment, ergonomics and occupational health and safety.
Student roles:
The student will participate in the writing of various summaries associated with the project as well as the various data collections required for the development of instrumented bicycles. Subsequently, he will have to collect this information and carry out various analyzes and evaluations in order to draw up data and feasibility reports. Once the data has been collected, he will collaborate in the writing of scientific articles, reports as well as texts for scientific conferences in which the research team will participate. Depending on his involvement, he may be required to present his work in different scientific or professional events.
Skills required:
The student must be able to work on an independent basis in carrying out a review of writings in the literature on various themes associated with the research and actively participate in the other activities and achievements of the research team. The student will have to demonstrate dynamism, curiosity and creativity in the face of the challenges raised by a project of this magnitude. Everything will be carried out in an interdisciplinary team where respect and constant collaboration are the cornerstones of the team. A background in human factors and ergonomics as well as a good knowledge of microcontroller platforms