Prediction Algorithm of Dance Movement Rhythm and Coordination Based on Time Series Analysis

Dance, an art form expressed through the human body, demands exceptional flexibility, posture, and movement coordination from its performers. Coordination stands as a pivotal aesthetic aspect of dance and a fundamental skill for dancers. The rich diversity of dance styles reflects the socio-cultural and historical backgrounds of various ethnic groups, offering distinct perspectives on life. In this study, we integrate a practical approach with theoretical analysis to investigate dance movement rhythm and coordination using time series data from dance training sessions. By employing advanced algorithms, we conducted an in-depth analysis focused on rotational stability and the center of gravity. Experimental results indicate that our algorithm consistently achieved a rotational stability score fluctuating around 9.09 points, with a similar performance in center of gravity tests, scoring approximately 9.25 points. In comparison, other techniques like Deep Learning (DL) and Genetic Algorithms (GA) exhibited a wider score range and lower overall scores. This underscores the superiority of our proposed algorithm in predicting dance movement rhythm and coordination. Mastering this technique is paramount for dance students seeking to enhance their future performances.