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Spatial-Temporal Graph Convolutional Framework for Yoga Action Recognition and Grading.

Shu Wang1

  • 1School of Physical Education, Inner Mongolia Minzu University, Tongliao, Inner Mongolia 028000, China.

Computational Intelligence and Neuroscience
|April 8, 2022
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Summary
This summary is machine-generated.

This study introduces a novel system for recognizing and grading yoga poses using a spatial-temporal graph convolutional neural network. The technology accurately identifies yoga movements and assesses pose correctness, preventing injuries from non-standard yoga practice.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Sports Science

Background:

  • Online yoga instruction is popular but poses risks for beginners due to difficulties in mastering standard poses from videos.
  • Non-standard yoga poses can lead to significant physical damage or disability.
  • There is a need for automated systems to provide real-time feedback on yoga pose execution.

Purpose of the Study:

  • To develop and evaluate a yoga action recognition and grading system.
  • To accurately identify and classify yoga poses using advanced neural network techniques.
  • To provide timely feedback on pose standardization to prevent user injury.

Main Methods:

  • Yoga movement data captured using depth cameras.
  • Frame-by-frame video labeling utilizing Long Short-Term Memory (LSTM) networks.
  • Skeletal joint point feature extraction via graph convolution, incorporating spatial-temporal dimensions and inter-frame correlations.

Main Results:

  • The proposed system demonstrates high accuracy in identifying and classifying yoga poses.
  • The system effectively distinguishes between standard and non-standard yoga poses.
  • Experimental validation confirms the system's capability to provide corrective feedback.

Conclusions:

  • The spatial-temporal graph convolutional neural network system offers a reliable solution for yoga pose analysis.
  • This technology can enhance the safety and effectiveness of online yoga learning.
  • The system has the potential to reduce yoga-related injuries by ensuring pose accuracy.