Multimedia interactive creative dance choreography integrating intelligent chaotic art algorithms

Creative dance choreography involves the exploration of movement as a form of artistic expression, often characterized by innovative and spontaneous elements. Interactive multimedia enhances the art form by integrating technology, enabling real-time audience engagement and dynamic visual effects. Traditional choreography often adheres to fixed patterns, which can limit improvisation and reduce audience participation. The objective of the study is to integrate the chaotic art algorithm within multimedia interactive dance choreography, enhancing both the creative process and audience engagement while producing novel movement patterns. Data were collected through motion capture technology and video recordings. The preprocessing phase included noise reduction and normalization of the movement data. Feature extraction using principal component analysis (PCA) techniques analyzed the captured data, identifying key attributes such as speed, trajectory, and synchronization. The study proposed a simulation-based intelligent chaotic optimized generative adversarial network (ICO-GAN) that enhances dance choreography by generating diverse, unpredictable movement patterns, optimizing performance through chaotic art algorithms, and fostering innovative, engaging interactions between dancers and multimedia elements. The result shows that the proposed ICO-GAN method enriched the choreography by generating diverse movement patterns that were seamlessly incorporated into performances based on precision (94%), recall (92%), accuracy (97%), and F1-score (93%). This innovative approach opens new avenues for exploration in dance and technology, offering expanded possibilities for artistic expression and interaction.