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<p>This paper presents a novel approach to evaluating and optimizing dance movements using deep learning techniques. We combine Convolutional Neu- ral Networks (CNNs) for spatial feature extraction, Long Short-Term Memory (LSTM) networks for temporal analysis, and Class Activation Maps (CAMs) for interpretability to assess dance aesthetics. Additionally, we employ reinforcement learning and few-shot learning to optimize both artistic expression and biome- chanical efficiency. Our approach provides an objective framework for dancers and choreographers, offering insights into the key movements that influence aes- thetic quality while minimizing injury risks. Experimental results show that the proposed model aligns closely with human evaluations, with a 15% improvement in aesthetic scores and a 10% reduction in biomechanical inefficiency.</p>

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Deep learning framework for aesthetic and biomechanical optimization of dance movements

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Ying Shen

摘要

This paper presents a novel approach to evaluating and optimizing dance movements using deep learning techniques. We combine Convolutional Neu- ral Networks (CNNs) for spatial feature extraction, Long Short-Term Memory (LSTM) networks for temporal analysis, and Class Activation Maps (CAMs) for interpretability to assess dance aesthetics. Additionally, we employ reinforcement learning and few-shot learning to optimize both artistic expression and biome- chanical efficiency. Our approach provides an objective framework for dancers and choreographers, offering insights into the key movements that influence aes- thetic quality while minimizing injury risks. Experimental results show that the proposed model aligns closely with human evaluations, with a 15% improvement in aesthetic scores and a 10% reduction in biomechanical inefficiency.