Emotion Recognition in Contemporary Dance Performances Using Laban Movement Analysis
This work addresses emotion recognition for dance performance analysis and training, but it is incremental as it builds on existing methods.
The paper tackled emotion recognition in contemporary dance by improving Laban Movement Analysis features and introducing new descriptors, achieving a highest accuracy of 96.85%.
This paper presents a novel framework for emotion recognition in contemporary dance by improving existing Laban Movement Analysis (LMA) feature descriptors and introducing robust, novel descriptors that capture both quantitative and qualitative aspects of the movement. Our approach extracts expressive characteristics from 3D keypoints data of professional dancers performing contemporary dance under various emotional states, and trains multiple classifiers, including Random Forests and Support Vector Machines. Additionally, we provide in-depth explanation of features and their impact on model predictions using explainable machine learning methods. Overall, our study improves emotion recognition in contemporary dance and offers promising applications in performance analysis, dance training, and human--computer interaction, with a highest accuracy of 96.85\%.