<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Since the development of Chinese classical dance, its dance technology and dance skills have been maturing for a long time, but the innovation and development of classical dance teaching mode needs to be improved. In this paper, OptiTrack is used to capture the dance movement data of classical dancers, transform the data into three-dimensional coordinates, and extract the features of classical dance data through direction normalization. The Hidden Markov Model obtains the time series information in the features and constructs the correlation model. Using the skeletal model data to get the center of mass of the human body, the convolution-gated recurrent unit network structure is used to improve the accuracy of the classical dance score generation results. Combining the above methods, a new teaching model framework for classical dance is proposed from three aspects and empirically analyzed. The empirical results show that the mean value of each dimension of classical dance teachers’ teaching ability ranges from 3.4562-4.3621, which is an excellent overall performance. In the comparison between students’ classical dance movements and standard movements, there existed higher scores than traditional movements in the two time periods within 20 minutes, which were 30 and 5 points higher than the conventional scores, respectively. The rest of the time was slightly lower than the regular movements, which shows that the innovation effect of classical dance teaching is better, and optimizes students’ classical dance movements to a certain extent.</p>
</abstract>

Since the development of Chinese classical dance, its dance technology and dance skills have been maturing for a long time, but the innovation and development of classical dance teaching mode needs to be improved. In this paper, OptiTrack is used to capture the dance movement data of classical dancers, transform the data into three-dimensional coordinates, and extract the features of classical dance data through direction normalization. The Hidden Markov Model obtains the time series information in the features and constructs the correlation model. Using the skeletal model data to get the center of mass of the human body, the convolution-gated recurrent unit network structure is used to improve the accuracy of the classical dance score generation results. Combining the above methods, a new teaching model framework for classical dance is proposed from three aspects and empirically analyzed. The empirical results show that the mean value of each dimension of classical dance teachers’ teaching ability ranges from 3.4562-4.3621, which is an excellent overall performance. In the comparison between students’ classical dance movements and standard movements, there existed higher scores than traditional movements in the two time periods within 20 minutes, which were 30 and 5 points higher than the conventional scores, respectively. The rest of the time was slightly lower than the regular movements, which shows that the innovation effect of classical dance teaching is better, and optimizes students’ classical dance movements to a certain extent.

Development and Innovation of Chinese Classical Dance Teaching Mode in the Background of Internet

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Since the development of Chinese classical dance, its dance technology and dance skills have been maturing for a long time, but the innovation and development...

Development and Innovation of Chinese Classical Dance Teaching Mode in the Background of Internet

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 15 gen 2024

Accettato: 23 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0615

Parole chiave
OptiTrack, Direction normalization, Hidden Markov model, Time series information, Classical dance teaching

© 2023 He Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Development and Innovation of Chinese Classical Dance Teaching Mode in the Background of Internet

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 15 gen 2024

Accettato: 23 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0615

Parole chiaveOptiTrack, Direction normalization, Hidden Markov model, Time series information, Classical dance teaching

© 2023 He Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
OptiTrack, Direction normalization, Hidden Markov model, Time series information, Classical dance teaching