autoWT: A Semi-Automated ML-Based Movement Tracking System for Performance Tracking and Analysis in Olympic Weightlifting

As part of AI Coaching Assistant project research in sports performance tracking systems, we present autoWT, a novel semi-automated computer vision tracking system designed and developed for repeated long-term performance tracking of Olympic Weightlifting (OW) training. The system integrates multiple cameras and a heart rate sensor to capture, detect, and analyse OWmovements, providing coaches and athletes with objective performance metrics. Key features include automated lift detection, clip extraction, and acquired performance metric visualisation based on markerless pose estimation data. The system architecture, consisting of a distributed system with multiple workers and a controller, enables efficient processing of high-bandwidth data streams. The paper provides an overall system architecture, operating principles and a detailed breakdown of action onset recognition and performance metric extraction system modules. We evaluate the system's lift detection accuracy and the repeatability of extracted performance metrics using data from Olympic lifts. Results demonstrate high accuracy in lift detection and consistent and repeatable metric extraction, indicating autoWT's potential as a valuable tool for conducting long-term Olympic weightlifting performance analysis studies and as an aid for coaching. The autoWT system can enhance the broader perspective and be an exemplary model for designing tracking systems in other sports.