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

<p>In this paper, we use motion capture technology to collect typical movements of Chinese folk dances and establish three-dimensional character models with the characteristics of human motion continuity and bone length invariance to discriminate the trajectory of skeletal movements. Video is used to export the data of Chinese folk dance. Design methods for extracting folk music and dance features to form a model for creating dances. Suppressing the noise of the original dance music, improving the accuracy of the fundamental frequency extraction of the music signal, and manually scoring the dance creation generation program are all benefits of using the Chebyshev filter. We propose the teaching method of Chinese folk dance in online broadcasting mode and carry out teaching experiments to compare the cooperative learning effect and dance technique evaluation effect between traditional dance teaching method and online broadcasting video lecture teaching method. The dance creation and generation program proposed in this paper is able to achieve a dance movement similarity of 0.92, which is able to preserve the original dance posture of Chinese folk dances to a greater extent. The results of the technical evaluation show that the teaching effects of the two teaching methods present significant differences in the four methods of basic technique, pair cooperation, dance expression, and total score, i.e., P&lt;0.05, which shows that the online live video explanation method is more suitable for the teaching of ethnic folk dance in the information age.</p>
</abstract>

In this paper, we use motion capture technology to collect typical movements of Chinese folk dances and establish three-dimensional character models with the characteristics of human motion continuity and bone length invariance to discriminate the trajectory of skeletal movements. Video is used to export the data of Chinese folk dance. Design methods for extracting folk music and dance features to form a model for creating dances. Suppressing the noise of the original dance music, improving the accuracy of the fundamental frequency extraction of the music signal, and manually scoring the dance creation generation program are all benefits of using the Chebyshev filter. We propose the teaching method of Chinese folk dance in online broadcasting mode and carry out teaching experiments to compare the cooperative learning effect and dance technique evaluation effect between traditional dance teaching method and online broadcasting video lecture teaching method. The dance creation and generation program proposed in this paper is able to achieve a dance movement similarity of 0.92, which is able to preserve the original dance posture of Chinese folk dances to a greater extent. The results of the technical evaluation show that the teaching effects of the two teaching methods present significant differences in the four methods of basic technique, pair cooperation, dance expression, and total score, i.e., P<0.05, which shows that the online live video explanation method is more suitable for the teaching of ethnic folk dance in the information age.

Research on Teaching and Dance Creation of Chinese Folk Dance in the Information Age

Applied Mathematics and Nonlinear Sciences

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

In this paper, we use motion capture technology to collect typical movements of Chinese folk dances and establish three-dimensional character models with the...

Research on Teaching and Dance Creation of Chinese Folk Dance in the Information Age

Pubblicato online: 30 mag 2024

Pagine: -

Ricevuto: 20 feb 2024

Accettato: 02 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1281

Parole chiave
Motion capture technology, 3D modeling, Feature extraction, Dance creation, Dance teaching

© 2024 Jianqiang Liu, published by Sciendo

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

Research on Teaching and Dance Creation of Chinese Folk Dance in the Information Age

Pubblicato online: 30 mag 2024

Pagine: -

Ricevuto: 20 feb 2024

Accettato: 02 mag 2024

DOI: https://doi.org/10.2478/amns-2024-1281

Parole chiaveMotion capture technology, 3D modeling, Feature extraction, Dance creation, Dance teaching

© 2024 Jianqiang Liu, published by Sciendo

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

Parole chiave
Motion capture technology, 3D modeling, Feature extraction, Dance creation, Dance teaching