Kinematic analysis of movement by using deep learning
Authors
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Email: 13021100015@163.com
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Zijie Song
Available Online 29 September 2024.
- DOI
- 10.2991/978-2-38476-291-0_23How to use a DOI?
- Keywords
- Kinematics; Dance; K-pop; ACGN; Deep Learning; PCA
- Abstract
The aesthetic standard for unconventional dances such as K-pop and ACGN dance does not follow the qualitative standard for traditional dances such as Ballet and modern dance. However, differential equations, motion capture, and computer vision can be used to quantitatively analyze these newly formed dances. Therefore, in the research, we use the AI method to extract and construct dance 3D poses and analyze the key points in dance by using PCA. The results indicate that PCA can identify the quality of dance. Moreover, the use of kinematics in dance aesthetics is considered to be effective.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Zijie Song PY - 2024 DA - 2024/09/29 TI - Kinematic analysis of movement by using deep learning BT - Proceedings of the 2024 3rd International Conference on Science Education and Art Appreciation (SEAA 2024) PB - Atlantis Press SP - 182 EP - 190 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-291-0_23 DO - 10.2991/978-2-38476-291-0_23 ID - Song2024 ER -