Performance Movement Analysis of Musicians via Image Processing

Abstract

Analyzing the movement of musicians during performance provides valuable insights into technique, ergonomics, and artistic expression. This study presents a novel approach to Performance Movement Analysis of Musicians using image processing techniques. The proposed system captures and processes video data to track body posture, hand positioning, and instrument interaction in real-time. Key methods include motion tracking, optical flow analysis, and pose estimation to assess movement efficiency and detect potential strain or inefficiencies in playing techniques. The system also enables quantitative evaluation of performance dynamics, offering applications in music education, injury prevention, and performance optimization. Results demonstrate the effectiveness of image processing in capturing fine motor movements and providing actionable feedback for musicians. Future work includes integrating machine learning models to enhance movement classification and personalization.