Archery training requires precision, consistency, and proper body form alignment to achieve optimal performance. This study introduces the Modern Archery Training Aid System (MATA©), a vision-based system that leverages computer vision and machine learning techniques to provide real-time feedback for archery body form analysis. The system integrates OpenCV and MediaPipe to detect human presence, estimate body height, and extract key joint positions for accurate body form evaluation. Through experimental trials, MATA© demonstrated an overall height estimation accuracy of 96.18%, with the most accurate measurement recorded at a 225 cm distance from the vision sensor. The system also dynamically adjusts the sensor orientation based on individual height variations to enhance adaptability across different users. Additionally, the integration of a pre-displayed reference skeleton enables beginner archers to train independently without direct coaching supervision, facilitating muscle memory development for improved shooting consistency. The system's robustness in various environmental conditions reinforces its potential as an effective training tool for archery. Future work will focus on refining body form estimation accuracy, expanding the dataset, and integrating additional features such as shot trajectory analysis to further enhance training effectiveness.
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