Deep learning framework for aesthetic and biomechanical optimization of dance movements

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.