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Deep Learning-Based Yoga Posture Recognition Using the Y_PN-MSSD Model for Yoga Practitioners.

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A new Y_PN-MSSD model provides automatic yoga posture recognition for online classes. This AI virtual yoga trainer offers real-time pose correction, improving practice and preventing health issues.

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Mobile-Net SSDPose-Netconvolutional neural networkdeep learningposture recognitiontensor flow lite

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Health & Wellness Technology

Background:

  • Online yoga instruction lacks real-time pose feedback, risking posture and health issues.
  • Beginner yoga practitioners struggle to self-assess pose accuracy without instructor guidance.
  • Existing technologies offer limited support for remote yoga practice assessment.

Purpose of the Study:

  • To propose an automatic yoga posture recognition system for online practitioners.
  • To develop a model for live-tracking and correcting yoga poses in real-time.
  • To enhance the accessibility and effectiveness of virtual yoga training.

Main Methods:

  • Developed the Y_PN-MSSD model, integrating Pose-Net for feature point detection and Mobile-Net SSD for human detection.
  • Collected and prepared data from four users and an open-source dataset featuring seven yoga poses.
  • Trained the model using extracted features from key body points for pose recognition.

Main Results:

  • Achieved 99.88% accuracy in recognizing and correcting yoga postures.
  • The Y_PN-MSSD model demonstrated superior performance compared to the Pose-Net CNN model.
  • The system provides live-tracking and on-the-fly pose correction for users.

Conclusions:

  • The Y_PN-MSSD model offers a viable solution for accurate, real-time yoga posture assessment.
  • This technology can serve as a foundation for developing affordable virtual yoga trainers.
  • The system empowers users to practice yoga safely and effectively online.