Integrating intelligent algorithms in music education to analyze and improve posture and motion in instrumental training
Abstract
This paper presents an innovative Artificial Intelligence (AI)—based system for real-time posture analysis and correction in instrumental music training. The system integrates OpenPose-based Convolutional Neural Networks (CNN) for skeletal tracking, Dynamic Time Warping for motion pattern analysis, and K-Nearest Neighbors (K-NN) for posture classification. Through a 16-week experimental study involving 18 music students, the system demonstrated significant improvements in learning outcomes compared to traditional methods. Key findings include (a) 33.3% faster technique acquisition in AI-assisted learning compared to traditional methods; (b) 18.6% higher posture improvement rates by week 16; (c) 40.2% better self-correction capabilities; and (d) 95.1% retention rate of correct posture after 6 months. The system processes video input at 120 fps with a total latency of 30 ms, achieving 94.3% accuracy in posture detection and 91.2% in motion pattern matching. The research establishes a comprehensive framework for integrating AI technology in music education, providing continuous, objective feedback during practice sessions. This approach addresses the critical gap between supervised instruction and individual practice, potentially reducing the risk of performance-related injuries through early detection of posture deviations.
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