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

Area of Science:

• Dance as a performing art form.
• Integration of artificial intelligence in arts education.
• Application of neural networks in movement analysis.

Background:

• Dance utilizes external body movements and internal modalities for expression, conveying information visually through silent language.
• Traditional dance teaching methods can be enhanced by technological integration to address complex control problems.
• Artificial intelligence (AI) offers novel approaches to analyzing and optimizing artistic expression.

Purpose of the Study:

• To investigate the application of artificial intelligence and the BP neural network (BPNN) algorithm in intelligent dance teaching.
• To evaluate the effectiveness of BPNN and PCA-BPNN algorithms in assessing dance training components.
• To explore how AI-driven dance instruction can improve sensory stimulation and artistic appreciation.

Main Methods:

• Implementation of the BP neural network (BPNN) algorithm for intelligent control in dance teaching.
• Utilizing both BPNN and PCA-BPNN algorithms to test dance language, music, and stage art training.
• Quantitative evaluation of model accuracy over time to assess performance.

Main Results:

• The BPNN evaluation model demonstrated an average accuracy of 85.35% at 80 time units.
• The PCA-BPNN evaluation model achieved an average accuracy of 65.64% under similar conditions.
• The BPNN model exhibited superior accuracy compared to the PCA-BPNN model in dance teaching evaluation.

Conclusions:

• The BPNN algorithm, integrated with AI, offers a more accurate evaluation model for dance teaching compared to PCA-BPNN.
• AI-enhanced dance instruction, particularly using BPNN, can lead to more intense sensory experiences for the audience.
• This approach fosters a harmonious integration of physical artistry and aesthetic enjoyment through technology.