Deep Learning Sequence Network for Identifying and Analyzing Archery Shooting Patterns

This study presents a deep learning-based system designed to enhance archery performance by analyzing athletes shooting motions and providing personalized feedback. Video data of four national-level Korean archers were collected between February and May 2024, and 17-joint coordinate data were extracted using pose estimation techniques. The full shooting sequence—from ready position to release—was captured and normalized for consistent analysis. Multiple deep learning sequence models, including RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU, were implemented and evaluated to determine the most effective approach for recognizing distinctive motion patterns of individual archers. The developed system enables objective quantification of motion characteristics, supporting personalized training feedback and performance enhancement. Hyperparameters were optimized using Optuna, and early stopping was applied to prevent overfitting. The system visualized motion consistency and identified joints with high error rates, allowing athletes to recognize and correct deviations in real time. By quantifying individual motion characteristics, the system facilitated the design of personalized training programs, ultimately improving technical performance. This approach offers a novel method for ongoing monitoring and performance evaluation, demonstrating significant potential not only for archery but also for other precision-based sports.