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<p>As a comprehensive art form integrating body movements, music rhythm, and emotional expression, dance has unique value in cultivating emotion-related competencies. However, traditional training methods have obvious deficiencies in objective assessment and personalized guidance. This study proposes a dance emotion-related performance training system based on multimodal perception and knowledge graph. Through integrating visual motion capture, physiological signal monitoring, and audio feature extraction, the system achieves multi-dimensional recognition of dancers’ emotional expression. A hierarchical attention mechanism is employed to adaptively fuse three modalities of information, achieving an emotion recognition accuracy of 89.6% under subject-independent evaluation. The system constructs a multi-level knowledge graph containing dance movement ontology, emotional expression rules, and genre-specific background knowledge. Through graph neural networks, it realizes semantic reasoning and rule matching, providing an interpretable analytical framework for emotional expression. The personalized training strategy based on reinforcement learning dynamically adjusts training content according to learners’ ability levels and cognitive characteristics, achieving collaborative optimization of skill cultivation and emotional development. In a six-month randomized controlled experiment, 120 dance major students from two Chinese universities participated in the system validation. The experimental group showed an overall emotion-related performance improvement of 25.8%, significantly higher than the control group’s 10.2% (t = 6.75, <i>p</i> &lt; 0.001, Cohen’s d = 1.23), and the training effect maintained an 87.3% high retention rate after three months. The research results suggest that technology-enabled dance training models can potentially promote the improvement of learners’ emotion-related performance in dance contexts, providing preliminary evidence for the potential of technology-enabled approaches in arts education. Limitations regarding construct validity of emotion-related performance measurement, sample representativeness, the absence of public benchmark evaluation, and the proprietary nature of the dataset are discussed.</p>

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Dance Emotion-Related Performance Training System Based on Multimodal Perception and Knowledge Graph

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月31日

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  • Yuan Tian

摘要

As a comprehensive art form integrating body movements, music rhythm, and emotional expression, dance has unique value in cultivating emotion-related competencies. However, traditional training methods have obvious deficiencies in objective assessment and personalized guidance. This study proposes a dance emotion-related performance training system based on multimodal perception and knowledge graph. Through integrating visual motion capture, physiological signal monitoring, and audio feature extraction, the system achieves multi-dimensional recognition of dancers’ emotional expression. A hierarchical attention mechanism is employed to adaptively fuse three modalities of information, achieving an emotion recognition accuracy of 89.6% under subject-independent evaluation. The system constructs a multi-level knowledge graph containing dance movement ontology, emotional expression rules, and genre-specific background knowledge. Through graph neural networks, it realizes semantic reasoning and rule matching, providing an interpretable analytical framework for emotional expression. The personalized training strategy based on reinforcement learning dynamically adjusts training content according to learners’ ability levels and cognitive characteristics, achieving collaborative optimization of skill cultivation and emotional development. In a six-month randomized controlled experiment, 120 dance major students from two Chinese universities participated in the system validation. The experimental group showed an overall emotion-related performance improvement of 25.8%, significantly higher than the control group’s 10.2% (t = 6.75, p < 0.001, Cohen’s d = 1.23), and the training effect maintained an 87.3% high retention rate after three months. The research results suggest that technology-enabled dance training models can potentially promote the improvement of learners’ emotion-related performance in dance contexts, providing preliminary evidence for the potential of technology-enabled approaches in arts education. Limitations regarding construct validity of emotion-related performance measurement, sample representativeness, the absence of public benchmark evaluation, and the proprietary nature of the dataset are discussed.