Martina Tumsich
Validation of Full-Body Markerless 3D MoCap for Paralympic AI Classification.
Rel. Laura Gastaldi. Politecnico di Torino, Master of science program in Biomedical Engineering, 2025
Abstract
The classification of impairments in Paralympic sport plays a fundamental role in ensuring fair competition, ensuring those who succeed do so due to their athletic excellence rather than because they are less impaired than their competitors. However, the current system still relies heavily on an authority-based decision-making process to determine both athlete eligibility and sport class allocation. To transition to a more objective and standardised approach, a new project was launched, led by the School of Human Movement and Nutrition Sciences at the University of Queensland (UQ). The aim is to develop an AI-based model that will ultimately support and complement the evidence-based Paralympic classification system.
To train an AI-based model, three-dimensional motion capture data is required
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