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Design and implementation of adolescent health Latin dance teaching system under artificial intelligence technology

Author

Listed:
  • Xutao Liu
  • Kim Geok Soh
  • Roxana Dev Omar Dev
  • Wenling Li
  • Qing Yi

Abstract

Since various dance teaching systems have attracted much attention with the development of Artificial Intelligence (AI) technology, this paper improves the recognition performance of Latin dance teaching systems by optimizing the action recognition model. Firstly, the object detection and action recognition technology under the current AI technology is analyzed, and the Two-stage object detection algorithm and One-stage object detection algorithm are evaluated. Secondly, the technologies and functions contained in the adolescent health Latin dance teaching system are described, including image acquisition, feature extraction, object detection, and action recognition. Finally, the action recognition algorithm is optimized based on object detection, and the rationality and feasibility of the proposed algorithm are verified by experiments. The experimental results show that the optimization algorithm can search the optimal feature subset after five iterations on Undefine Classes of 101 (UCF101) dataset, but it needs seven iterations on Human Motion Database 51 (HMDB51) dataset. Meanwhile, when using support vector machine classifier, the optimization algorithm can achieve the highest accuracy of motion recognition. Regressive Function, Multinomial Naive Bayes and Gaussian Naive Bayes Algorithms have lower prediction delay, as low as 0.01s. Therefore, this paper has certain reference significance for the design and implementation of adolescent health Latin dance teaching system.

Suggested Citation

  • Xutao Liu & Kim Geok Soh & Roxana Dev Omar Dev & Wenling Li & Qing Yi, 2023. "Design and implementation of adolescent health Latin dance teaching system under artificial intelligence technology," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-17, November.
    • Handle: RePEc:plo:pone00:0293313
    DOI: 10.1371/journal.pone.0293313
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    References listed on IDEAS

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    1. Mohd Anul Haq & Ilyas Khan & Ahsan Ahmed & Sayed M. Eldin & Ali Alshehri & Nivin A. Ghamry, 2023. "Dcnnbt: A Novel Deep Convolution Neural Network-Based Brain Tumor Classification Model," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 31(06), pages 1-26.
    2. Yan Zhang & M. M. Kamruzzaman & Lu Feng & Zhihan Lv, 2021. "Complex System of Vertical Baduanjin Lifting Motion Sensing Recognition under the Background of Big Data," Complexity, Hindawi, vol. 2021, pages 1-10, February.
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