Real-time augmented reality-enabled sports exercise monitoring system with personalized recommendations

Introduction: the study examined the integration of augmented reality technologies with deep learning in sports exercise monitoring, emphasizing its importance for improving precision, accuracy of execution, and motivation in both professional and educational contexts. Objective: the objective of the research was to develop and evaluate a real-time system for monitoring and classifying sports exercises with personalized recommendations. the scope included assessing the system’s effectiveness in improving exercise accuracy, learning retention, and participant motivation during pedagogical experiments. Methodology: the system applied pose estimation, joint angle validation, and action classification using deep learning models integrated with augmented reality feedback. a controlled pedagogical experiment was carried out with two groups, one experimental group using the system and one control group with traditional instruction. data collection involved pre-tests, post-tests, delayed post-tests, and motivational questionnaires. Results: the experimental group showed significant improvements in exercise accuracy, surpassing the control group’s performance. after two weeks, retention of correct technique remained higher, and participants reported increased interest, enjoyment, and perceived usefulness of training with the proposed system. Discussion: the results were consistent with previous literature emphasizing the role of immersive and interactive technologies in improving motor learning and motivation. however, this research extended earlier findings by confirming that real-time augmented reality guidance enhances both technical performance and long-term retention of exercise skills. Conclusions: the study concluded that the proposed system represents an effective and innovative pedagogical tool for advancing sports training, and its application should be expanded to broader educational and professional environments.