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. 2022 Jul 22:2022:4943413.
doi: 10.1155/2022/4943413. eCollection 2022.

An Adaptive Dance Motion Smart Detection Method Using BP Neural Network Model under Dance Health Teaching Scene

Shasha Liu  1
Affiliations

An Adaptive Dance Motion Smart Detection Method Using BP Neural Network Model under Dance Health Teaching Scene

Shasha Liu. J Environ Public Health. .

Retraction in

Abstract

As a body movement art, dance has its special form of expression. In terms of dance vocabulary, it can be roughly divided into two parts: external body movement and internal modality. In the process of body movement, it conveys information through silent language and the audience directly feels the information given by the dance image through vision. This is the special way of expressing emotion and meaning in dance art. This paper combines artificial intelligence technology and BP neural network (BPNN) algorithm to intelligently control dance teaching and solve complex nonlinear control problems. This paper studies dance teaching based on artificial intelligence technology. In this paper, BPNN algorithm and PCA-BPNN algorithm are used to test the dance teaching training of dance language, dance music, and stage art. The average accuracy of the BPNN evaluation model is 85.35% when the time reaches 80, while the average accuracy of the PCA-BPNN evaluation model is 65.64%. This shows that the accuracy of the BPNN evaluation model is higher than that of the PCA-BPNN evaluation model. Under the artificial intelligence technology, the dance using BPNN algorithm brings more intense sensory stimulation to the viewer because of the accompaniment of music, so as to achieve the infection and enjoyment of beauty and achieve the harmonious unity of sports and art.

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Conflict of interest statement

The author declares no conflicts of interest.

Figures

Figure 1
Figure 1
BPNN model.
Figure 2
Figure 2
Structure diagram of the BPNN intelligent teaching system.
Figure 3
Figure 3
Convergence diagram of training results of the BPNN evaluation model.
Figure 4
Figure 4
Convergence diagram of training results of the BPCA-BPNN evaluation model.
Figure 5
Figure 5
Music signal after frequency division processing.
Figure 6
Figure 6
Accuracy of the BPNN evaluation model.
Figure 7
Figure 7
Accuracy of the PCA-BPNN evaluation model.
See this image and copyright information in PMC

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