Yoga Pose Detection and Classification Using Deep Learning

Authors

  • Deepak Kumar  Department of Information Technology, Research Scholar, Amity University, Jharkhand, India
  • Anurag Sinha   Department of Information Technology, Research Scholar, Amity University, Jharkhand, India

DOI:

https://doi.org/10.32628/CSEIT206623

Keywords:

Pose, Self-Learning, Posenet, Deep Learning, Pose Classification.

Abstract

Yoga is an ancient science and discipline originated in India 5000 years ago. It is used to bring harmony to both body and mind with the help of asana, meditation and various other breathing techniques It bring peace to the mind. Due to increase of stress in the modern lifestyle, yoga has become popular throughout the world. There are various ways through which one can learn yoga. Yoga can be learnt by attending classes at a yoga centre or through home tutoring. It can also be self-learnt with the help of books and videos. Most people prefer self-learning but it is hard for them to find incorrect parts of their yoga poses by themselves. Using the system, the user can select the pose that he/she wishes to practice. He/she can then upload a photo of themselves doing the pose. The pose of the user is compared with the pose of the expert and difference in angles of various body joints is calculated. Based on thisdifference of angles feedback is provided to the user so that he/she can improve the pose.

References

  1. L. Sigal. “Human pose estimation”, Ency. of Comput. Vision, Springer 2011.
  2. S. Yadav, A. Singh, A. Gupta, and J. Raheja, “Real-time yoga recognition using deep learning”, Neural Comput. and Appl., May 2019.Online]. Available: https://doi.org/10.1007/s00521-019-04232-7
  3. U. Rafi, B. Leibe, J.Gall, and I. Kostrikov, “An efficient convolutional network for human pose estimation”, British Mach. Vision Conf., 2016.
  4. S. Haque, A. Rabby, M. Laboni, N. Neehal, and S. Hossain, “ExNET: deep neural network for exercise pose detection”, Recent Trends in Image Process. and Pattern Recog., 2019.
  5. M. Islam, H. Mahmud, F. Ashraf, I. Hossain and M. Hasan, "Yoga posture recognition by detecting human joint points in real time using microsoft kinect", IEEE Region 10 Humanit. Tech. Conf., pp. 668-67, 2017.
  6. S. Patil, A. Pawar, and A. Peshave, “Yoga tutor: visualization and analysis using SURF algorithm”,Proc. IEEE Control Syst. Graduate Research Colloq.,pp. 43-46, 2011.
  7. W. Gong, X. Zhang, J. Gonzàlez, A. Sobral, T. Bouwmans, C. Tu, and H. Zahzah, “Human pose estimation from monocular images: a comprehensive survey”, Sensors, Basel, Switzerland, vol. 16, 2016.
  8. G. Ning, P. Liu, X. Fan and C. Zhan, “A top-down approach to articulated human pose estimation and tracking”, ECCV Workshops, 2018.
  9. A. Gupta, T. Chen, F. Chen, and D. Kimber, “Systems and methods for human body pose estimation”, U.S. patent, 7,925,081 B2, 2011.
  10. H. Sidenbladh, M. Black, and D. Fleet, “Stochastic tracking of 3D human figures using 2D image motion”, Proc 6th European Conf. Computer Vision, 2000.
  11. A. Agarwal and B. Triggs, “3D human pose from silhouettes by relevance vector regression”, Intl Conf. on Computer Vision & Pattern Recogn.pp.882–888, 2004.
  12. M. Li, Z. Zhou, J. Li and X. Liu, “Bottom-up pose estimation of multiple person with bounding box constraint”, 24th Intl. Conf. Pattern Recogn.,2018.
  13. Z. Cao, T. Simon, S. Wei, and Y. Sheikh, “OpenPose: realtime multi-person 2D pose estimation using part affinity fields”, Proc. 30th IEEE Conf. Computer Vision and Pattern Recogn,2017.
  14. A. Kendall, M. Grimes, R. Cipolla, “PoseNet: a convolutional network for real-time 6DOF camera relocalization”, IEEE Intl. Conf. Computer Vision, 2015.
  15. S. Kreiss, L. Bertoni, and A. Alahi, “PifPaf: composite fields for human pose estimation”, IEEE Conf. Computer Vision and Pattern Recogn, 2019.
  16. P. Dar, “AI guardman – a machine learning application that uses pose estimation to detect shoplifters”.Online]. Available: https://www.analyticsvidhya.com/blog/2018/06/ai-guardman-machine-learning application-estimates-poses-detect-shoplifters/
  17. D. Mehta, O. Sotnychenko, F. Mueller and W. Xu, “XNect: real-time multi-person 3D human pose estimation with a single RGB camera”, ECCV, 2019.
  18. A. Lai, B. Reddy and B. Vlijmen, “Yog.ai: deep learning for yoga”.Online]. Available: http://cs230.stanford.edu/projects_winter_2019/reports/15813480.pdf
  19. M. Dantone, J. Gall, C. Leistner, “Human pose estimation using body parts dependent joint regressors”, Proc. IEEE Conf. Computer Vision Pattern Recogn., 2013.
  20. A. Mohanty, A. Ahmed, T. Goswami, “Robust pose recognition using deep learning”, Adv. in Intelligent Syst. and Comput, Singapore, pp 93-105, 2017.
  21. P. Szczuko, “Deep neural networks for human pose estimation from a very low resolution depth image”, Multimedia Tools and Appl, 2019.
  22. M. Chen, M. Low, “Recurrent human pose estimation”,Online]. Available: https://web.stanford.edu/class/cs231a/prev_projects_2016/final%20(1).pdf
  23. K. Pothanaicker, “Human action recognition using CNN and LSTM-RNN with attention model”, Intl Journal of Innovative Tech. and Exploring Eng, 2019.
  24. N. Nordsborg, H. Espinosa, “Estimating energy expenditure during front crawl swimming using accelerometrics”, Procedia Eng., 2014.
  25. P. Pai, L. Changliao, K. Lin, “Analyzing basketball games by support vector machines with decision tree model”, Neural Comput. Appl., 2017.
  26. S. Patil, A. Pawar, A. Peshave, “Yoga tutor: visualization and analysis using SURF algorithm”, Proc. IEEE Control Syst. Grad. Research Colloquium, 2011.
  27. W. Wu, W. Yin, F. Guo, “Learning and self-instruction expert system for yoga”, Proc. Intl. Work Intelligent Syst. Appl, 2010.
  28. E. Trejo, P. Yuan, “Recognition of yoga poses through an interactive system with kinect device”, Intl. Conf. Robotics and Automation Science, 2018.
  29. H. Chen, Y. He, C. Chou, “Computer assisted self-training system for sports exercise using kinetics”, IEEE Intl. Conf. Multimedia and Expo Work, 2013.
  30. DatasetOnline]. Available: https://archive.org/details/YogaVidCollected.
  31. Y. Shavit, R. Ferens, “Introduction to camera pose estimation with deep learning”,Online]. Available: https://arxiv.org/pdf/1907.05272.pdf.
  32. Gao Z, Zhang H, Liu AA et al (2016) Human action recognition on depth dataset. Neural Comput Appl 27:2047–2054. https://doi. org/10.1007/s00521-015-2002-0
  33. Poppe R (2010) A survey on vision-based human action recognition. Image Vis Comput 28:976–990. https://doi.org/10.1016/j. imavis.2009.11.014
  34. Weinland D, Ronfard R, Boyer E (2011) A survey of visionbased methods for action representation, segmentation and recognition. Comput Vis Image Underst 115:224–241. https://doi. org/10.1016/j.cviu.2010.10.002
  35. Halliwell E, Dawson K, Burkey S (2019) A randomized experimental evaluation of a yoga-based body image intervention. Body Image 28:119–127. https://doi.org/10.1016/j.bodyim.2018.12. 005
  36. Patil S, Pawar A, Peshave A et al (2011) Yoga tutor: visualization and analysis using SURF algorithm. In: Proceedings of 2011 IEEE control system graduate research colloquium, ICSGRC 2011, pp 43–46
  37. Chen HT, He YZ, Hsu CC et al (2014) Yoga posture recognition for self-training. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp 496–505
  38. Schure MB, Christopher J, Christopher S (2008) Mind–body medicine and the art of self-care: teaching mindfulness to counseling students through yoga, meditation, and qigong. J Couns Dev. https://doi.org/10.1002/j.1556-6678.2008.tb00625.x
  39. Chen HT, He YZ, Hsu CC (2018) Computer-assisted yoga training system. Multimed Tools Appl 77:23969–23991. https:// doi.org/10.1007/s11042-018-5721-2
  40. Maanijou R, Mirroshandel SA (2019) Introducing an expert system for prediction of soccer player ranking using ensemble learning. Neural Comput Appl. https://doi.org/10.1007/s00521019-04036-9
  41. Nordsborg NB, Espinosa HG, Thiel DV (2014) Estimating energy expenditure during front crawl swimming using accelerometers. Procedia Eng 72:132–137. https://doi.org/10.1016/j.proeng.2014. 06.024
  42. Connaghan D, Kelly P, O’Connor NE et al (2011) Multi-sensor classification of tennis strokes. Proc IEEE Sens. https://doi.org/ 10.1109/icsens.2011.6127084
  43. Shan CZ, Su E, Ming L (2015) Investigation of upper limb movement during badminton smash. In: 2015 10th Asian Control conference, pp 1–6. https://doi.org/10.1109/ascc.2015.7244605 .

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-12-30

Issue

Volume 6, Issue 6, November-December-2020

Section

Research Articles

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How to Cite

[1]
Deepak Kumar, Anurag Sinha , " Yoga Pose Detection and Classification Using Deep Learning " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 6, pp.160-184, November-December-2020. Available at doi : https://doi.org/10.32628/CSEIT206623