Abstract
Dance, as a highly expressive form of art, conveys intense emotions through bodily movements and postures. In the field of human-computer interaction, the automated recognition of dance movements poses a significant challenge concerning artistic expression and emotional classification. Analyzing dance movements enables us to extract rich emotional information. This paper introduces a novel approach for dance emotion recognition-the Laban Movement Analysis (LMA)-which characterizes the human body based on three aspects: body distribution, body structure, and dynamic trends. Leveraging artificial intelligence-based computer vision technology, we conduct a comparative analysis and supervised learning on existing dance performance video datasets. Various machine learning algorithms are trained and compared. The results indicate that recognizing emotional information from the perspective of dance movements achieves a high level of accuracy.
