Emotion Recognition in Contemporary Dance Performances Using Laban Movement Analysis

2025-04-29

AI Summary

This work addresses the low accuracy and poor interpretability of emotion recognition in contemporary dance performances. We propose an interpretable multi-class classification method based on 3D skeletal keypoint data. Our approach innovatively extends the Laban Movement Analysis (LMA) framework by systematically integrating qualitative Effort features with quantitative kinematic descriptors, yielding a novel LMA-based descriptor. We further combine ensemble learning (Random Forest/SVM) with SHAP—a model-agnostic explainable AI technique—to enable transparent, semantically grounded emotion modeling. Evaluated on a professional dancer dataset, our method achieves 96.85% classification accuracy, substantially outperforming conventional LMA-based approaches. Beyond improving recognition performance, our framework enhances decision interpretability by attributing predictions to human-interpretable movement qualities. This advances dance analytics, enables intelligent, feedback-driven pedagogy, and establishes a new paradigm for affective human–computer interaction grounded in embodied movement semantics.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Intelligent Robots: Embodied AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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%.

Problem

• Improving emotion recognition in contemporary dance using enhanced Laban Movement Analysis
• Extracting expressive features from 3D keypoints data for emotion classification
• Applying explainable machine learning to interpret dance movement features

Innovation

• Enhanced Laban Movement Analysis descriptors
• 3D keypoints data for emotion extraction
• Explainable machine learning for feature impact