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Yoga Pose Estimation and Feedback Generation Using Deep Learning.

Vivek Anand Thoutam1, Anugrah Srivastava1, Tapas Badal1

  • 1Computer Science Engineering Department, Bennett University, Greater Noida, India.

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Summary
This summary is machine-generated.

This study introduces a deep learning system to detect incorrect yoga postures, offering personalized feedback for safer practice. The AI achieves high accuracy, aiding users in correcting poses and preventing injuries.

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Area of Science:

  • Integrative medicine
  • Computer science
  • Artificial intelligence

Background:

  • Yoga, originating in ancient India, is a 5000-year-old practice for mind-body balance.
  • Modern lifestyles increase stress, driving global interest in yoga for well-being.
  • Self-learning yoga is popular but risks incorrect postures, potentially causing harm.

Purpose of the Study:

  • To develop deep learning techniques for accurate yoga posture detection.
  • To provide users with real-time feedback on pose correctness.
  • To enhance the safety and effectiveness of self-taught yoga practice.

Main Methods:

  • Utilized deep learning models to analyze user-submitted yoga practice videos.
  • Developed algorithms to detect abnormal joint angles compared to correct poses.
  • Implemented a system to provide specific feedback on pose correction.

Main Results:

  • The proposed deep learning method achieved a high accuracy of 0.9958 in detecting incorrect yoga postures.
  • The system demonstrated superior accuracy compared to existing state-of-the-art methods.
  • The approach requires less computational complexity than comparable techniques.

Conclusions:

  • Deep learning offers an effective solution for identifying and correcting yoga posture errors.
  • This technology can significantly improve the safety of independent yoga practice.
  • The system provides valuable guidance for users to refine their yoga poses accurately.