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A Computer Vision-Based Yoga Pose Grading Approach Using Contrastive Skeleton Feature Representations.

Yubin Wu1, Qianqian Lin1, Mingrun Yang1

  • 1Institute of Systems Science, National University of Singapore, Singapore 119615, Singapore.

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Summary
This summary is machine-generated.

This study introduces a computer vision method for yoga pose grading using skeleton keypoints. The approach enhances accuracy by employing novel coarse and fine triplet examples for feature encoding.

Keywords:
contrastive learningdeep learningskeleton extractionyoga pose classificationyoga pose grading

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Area of Science:

  • Computer Vision
  • Biomechanics
  • Artificial Intelligence

Background:

  • Yoga pose grading traditionally relies on human expertise, which can be subjective and time-consuming.
  • Objective, quantitative assessment of yoga poses is crucial for effective instruction and injury prevention.
  • Existing automated methods often struggle with the nuanced variations in human poses.

Purpose of the Study:

  • To develop a computer vision-based system for objective yoga pose grading.
  • To propose a novel approach using contrastive skeleton feature representations for accurate pose assessment.
  • To introduce a new strategy for generating contrastive examples to improve pose feature encoding.

Main Methods:

  • Extraction of human body skeleton keypoints from yoga pose images.
  • Utilizing a pose feature encoder trained with contrastive triplet examples (coarse and fine).
  • Comparison of encoded pose features to provide a quantitative grade.

Main Results:

  • The proposed computer vision approach demonstrates superior performance in yoga pose grading.
  • The novel strategy for composing coarse and fine triplet examples effectively addresses challenges in pose feature encoding.
  • Experiments on benchmark datasets validate the effectiveness and accuracy of the method.

Conclusions:

  • The developed computer vision-based yoga pose grading system offers an objective and quantitative evaluation method.
  • Contrastive skeleton feature representations, enhanced by a new triplet example strategy, significantly improve pose grading accuracy.
  • This approach has the potential to aid yoga practitioners and instructors in achieving better pose alignment and quality.