Kinematic analysis of movement by using deep learning

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.

Download article (PDF)

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  -