Optimizing Dance Training Programs Using Deep Learning: Exploring Motion Feedback Mechanisms Based on Pose Recognition and Prediction

Dance pose recognition and prediction is an important part of dance training and a challenging task in the field of artificial intelligence. Due to the diverse styles and significant variations in dance movements, conventional methods struggle to capture effective dance pose features for recognition. In this context, we have developed a dance pose recognition and prediction method based on deep learning. Given the characteristics of dance movements, such as complex human postures and dynamic movements, we proposed the MKFF-ST-GCN model, which integrates multi-kinematic feature fusion with ST-GCN. This model fully captures the dynamic information of dance movements by calculating the first and second-order kinematic features of keypoints and fuses the kinematic features using a multi-head attention mechanism. Additionally, to address dance pose prediction issues, we proposed the STGA-Net based on the spatial-temporal graph attention mechanism. This model improves the long-distance information modeling capability by calculating local and global graph attentions of dance poses, effectively solving the problem of dance pose prediction. To comprehensively evaluate the quality of the proposed methods in dance pose recognition and prediction, we conducted extensive experimental validations and comparisons with several common algorithms. The experimental results fully demonstrate the effectiveness of our methods in dance pose recognition and prediction. This study not only advances the technology of dance pose recognition and prediction but also provides valuable experience for the field.

@article{Jiao2024,
  title     = {Optimizing Dance Training Programs Using Deep Learning: Exploring Motion Feedback Mechanisms Based on Pose Recognition and Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yuting Jiao},
  doi       = {10.14569/IJACSA.2024.0150846},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150846}
}