Topology-Preserving Circular Emotion Representation for Modern Dance via Interpretable Laban Prototypes

Modern dance conveys complex emotions through continuous bodily movement, but assigning recorded motion to continuous affective coordinates remains challenging. This study examines the mapping of Laban Movement Analysis (LMA)-informed kinematic descriptors to predefined emotion prototypes in Russell's valence-arousal space. Thirty-one frame-level descriptors were extracted from recorded dance motion and temporally aggregated for circular-target regression. Four-quadrant classification and direct valence-arousal regression were also evaluated as separate modeling tasks. In the 12-category circular experiment, the selected ExtraTrees model was evaluated on 117 motion sequences using five-fold stratified cross-validation and obtained an angular mean absolute error of 36.765 ± 6.414° and a quadrant macro-F1 of 0.670 ± 0.094. These values measure prediction against assigned category-level affective targets. Shortest-arc interpolation of predefined prototype angles, combined with linear interpolation of their radii, produced continuous numerical paths in valence-arousal space; generated skeletal transitions were not evaluated. A separate questionnaire involving 36 observers assessed 24 videos of the recorded dancers. The findings provide preliminary evidence that the selected descriptors recover assigned affective direction in this recorded-dance dataset. Perceptual evaluation of generated skeletal or avatar motion and investigation of potential applications in immersive or digital-human settings remain future work.