Research on Movement Fluidity Assessment for Professional Dancers Based on Artificial Intelligence Technology

Abstract

A novel approach to movement fluidity assessment for professional dancers utilizing artificial intelligence technology is proposed. The system mixed captured systems with performance algorithms to measure dance works well by various observations. The framework works in a hierarchical architecture information is recommended, actions, reviews, and structures. The information is suggested by multiple-modal techniques of optimal optical measurements and analyzed using neural networks. The system jointly combines movement, measurements such as complex action, physical activity, strength Energy, and strength to make beautiful, and strength to make beautiful, and strength. Experimental validation involving 75 professional and semi-professional dancers across multiple dance styles demonstrates the system's effectiveness in movement quality assessment. The results show significant improvements in assessment accuracy (92%) compared to baseline methods (85%), with high precision (0.89) and recall (0.91) in movement quality evaluation. The proposed system enables real-time analysis and feedback while maintaining computational efficiency, and processing movement data within 20-25ms. This research advances the field of automated dance performance assessment by providing objective, quantitative metrics for movement quality evaluation while maintaining alignment with professional dance standards.