Developing Motion Templates of Sport Training Using R-GDL Approach for Evaluating Extrinsic Feedback of Penalty Kicks

The study developed Motion Templates (MTs) using the Reverse-Gesture Description Language (R-GDL) method to evaluate extrinsic feedback in football penalty kick training. Traditional coaching methods often rely on subjective and qualitative assessments. To address this, motion capture (MoCap) technology was employed to collect kinematic data from two university football players (right- and left-footed) performing penalty kicks toward left (Set 1) and right (Set 2) goalpost and Score Rubric Assessment (SRA) form was used by professional coach to evaluate the performance. From the collected MoCap data, 40 successful penalty kicks were selected, converted into SKL format and generate MTs through Gesture Description Language (GDL) system using R-GDL, which standardized movement patterns through adaptive machine-learning-derived rules. The MTs incorporated features such as joint angles and limb trajectories, producing five rules per template for comparative analysis. Results demonstrated that MTs effectively differentiated players’ techniques across sets (e.g., Player A required fewer attempts in Set 1 than Player B in Set 2). Cross-validation against coach-evaluated Score Rubric Assessment (SRA) outcomes revealed that extrinsic feedback scores from MTs did not surpass SRA benchmarks, confirming the uniqueness of each player’s motion patterns. This highlights MTs’ reliability in providing objective, granular feedback for skill improvement. The study concludes that R-GDL-based MTs offer a robust tool for enhancing sports training analytics, enabling data-driven coaching strategies. Future work will focus on scalability, cost reduction, and extending this approach to other sports.