Nonlinear analysis of classic ballet movement and Kansei information using rough set theory

Classical ballet and other forms of dance have both aspects of sports competition and artistic performance. Dancers require not only the technical skill to control body movements but also the expressive ability to effectively and impressively convey emotions and narratives to an audience. While physical movements can be measured and quantitatively evaluated, it is challenging to quantitatively assess people's subjective impressions of dance. Addressing this challenge, research aimed to quantify and objectively evaluate ambiguous Kansei information, handle nonlinear relationships, and specifically analyze skeletal movements in dance. To investigate the impressions ballet, a questionnaire survey was conducted. This involved using 18 pairs of Kansei words to evaluate nine sample videos, which allowed for the definition of decision classes. Subsequently, rough set theory was applied to analyze the relationship between ballet body movements and these impressions. Analysis focused solely on skeletal movements, deliberately excluding elements like background, music, and costumes that significantly influence overall impressions. OpenPose was utilized to detect human joint points and generate a BODY_25 Model. Dance characteristics were then defined using 8 attributes, each with two possible values to thoroughly analyze the correlation between body movement features and the impressions perceived by viewers. By utilizing rough set theory, the ability to extract patterns and regularities from data containing subjective human judgments is demonstrated. This enables an objective evaluation of the relationship between body movements and Kansei information such as "beautiful" or "elegant" derived from ballet's physical expressions. This approach proves that rough set theory's robust analytical capabilities can effectively analyze the nonlinear relationship between body movements and viewer impressions.