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A convolutional neural network based online teaching method using edge-cloud computing platform.

Liu Zhong1

  • 1Shandong University of Arts, Jinan, China.

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|April 3, 2023
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Summary
This summary is machine-generated.

This study introduces an AI-powered online dance teaching method using edge computing for personalized feedback. The cloud-edge platform enhances learning efficiency and student experience by analyzing dance movements.

Keywords:
Dance action correctionEdge calculationGrid codingKey frame extraction

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Education Technology

Background:

  • Traditional dance teaching faces challenges with diverse student learning paces and limited teacher attention, leading to varied learning outcomes.
  • Existing online methods often lack personalized, real-time feedback crucial for skill development in dance.
  • The need for adaptive and efficient online learning solutions is increasing in specialized fields like dance education.

Purpose of the Study:

  • To propose and evaluate an AI-driven online dance teaching system leveraging edge computing.
  • To address the limitations of traditional and current online dance education by providing targeted, individualized instruction.
  • To enhance the effectiveness and engagement of online dance learning through intelligent feedback mechanisms.

Main Methods:

  • Utilized deep convolutional neural networks (CNNs) for key frame extraction from dance videos.
  • Employed grid coding and fully convolutional networks for human key point extraction and posture prediction.
  • Implemented a distributed CNN model with cloud-based training and edge-based prediction, incorporating guidance vectors for movement correction.
  • Integrated a cloud-edge computing platform for efficient model training on large datasets and utilized questionnaires to assess student learning status.

Main Results:

  • The developed cloud-edge platform demonstrated enhanced overall application performance and intelligence.
  • The system effectively supported novel teaching methodologies in dance education.
  • Experimental results indicated a significant improvement in the online dance learning experience for students.
  • The AI-driven approach facilitated efficient learning by addressing individual student weaknesses.

Conclusions:

  • The proposed AI and edge computing-based online dance teaching method offers an effective solution for personalized dance education.
  • The cloud-edge architecture optimizes AI model performance for real-time feedback and adaptive learning.
  • This approach has the potential to revolutionize online dance instruction by improving learning efficiency and student outcomes.