Representation of yoga postures based on joint angle values along with Range of motion limitations of joints to classify time series yoga
DOI:
https://doi.org/10.61841/18t2g668Keywords:
Representation of yoga postures based on joint angle values along with Range of motion limitations of joints to classify time series yoga asanasAbstract
The practice of yoga poses, or asanas, was developed as an approach to align, strengthen, and balance the structure of the body. Further, it has been used to enhance dynamic control of core stabilizing muscles to reduce lower back pain (LBP) and spinal flexibility. Yoga postures comprise simple body movements such as standing, sitting, forward and backbend, twist, inversion, and lying down in supine position. In this paper proposes a method to classify yoga asana postures based on joint angles. Not every joint is involved in all asanas. Set of joints that contributes a lot for a particular asana and their range of motion has studied in order to classify asanas based on the joints involved. Applicability using BVH and softmax classification has been discussed.
Downloads
References
1. Cramer H, Lauche R, Haller H, Dobos G. A systematic review and meta-analysis of yoga for low back pain. Clin J Pain. 2013;29:450–60.
2. Williams K, Abildso C, Steinberg L, Doyle E, Epstein B, Smith D, et al. Evaluation of the effectiveness and efficacy of Iyengar yoga therapy on chronic low back pain. Spine (Phila Pa 1976) 2009;34:2066–76.
3. Galantino ML, Bzdewka TM, Eissler-Russo JL, Holbrook ML, Mogck EP, Geigle P, et al. The impact of modified Hatha yoga on chronic low back pain: A pilot study. AlternTher Health Med. 2004;10:56–9.
4. Vakos JP, Nitz AJ, Threlkeld AJ, Shapiro R, Horn T. Electromyographic activity of selected trunk and hip muscles during a squat lift. Effect of varying the lumbar posture. Spine (Phila Pa 1976) 1994;19:687–95
5. Nashner LM, McCollum G. The organization of human postural movements: A formal basis and experimental synthesis. Behav Brain Sci 1985;8:135-72.
6. Gatev P, Thomas S, Kepple T, Hallett M. Feedforward ankle strategy of balance during quiet stance in adults. J Physiol 1999;514:915-28.
7. Barnett C, Napier J. The axis of rotation at the ankle joint in man.Its influence upon the form of the talus and the mobility of the fibula. J Anat 1952; 86(Pt 1): 1.
8. Nigg B, Fisher V, Ronsky J. Gait characteristics as a function of age and gender. Gait Posture 1994; 2: 213-20.
9. Chopra S, Rouhani H, Assal M, Aminian K, Crevoisier X.Outcome of unilateral ankle arthrodesis and total anklereplacement in terms of bilateral gait mechanics. J Orthop ResMar 2014; 32: 377-84.
10. Tung-Wu Lu, Chu-Fen Chang. Biomechanics of human movement and its clinical applications. Kaohsiung Journal of Medical Sciences (2012) 28, S13-S25.
11. Watkins J. Structure and function of the musculoskeletal system. Champaign: Human Kinetics; 1999.
12. D.Wu,L.Shao,Leveraging hierarchical parametric networks for skeletal joints based action segmentation and recognition, in:Proceedings of Con ference on Computer Vision and Pattern Recognition(CVPR),IEEE,Colum- bus, Ohio.2014,pp.724–731.
13. C. Hecker, B. Raabe, R.W. Enslow, J. DeWeese, J. Maynard, K. van Prooijen, Real-time motion retargeting to highly varied user-created morphologies, ACM Trans. Graph. 27 (3) (2008) 27,
http://dx.doi.org/10.1145/ 1399504.1360626
14. G. Shakhnarovich, P. A. Viola, and T. J. Darrell. Fast pose estimation with parameter-sensitive hashing. In ICCV, 2003
15. G. Mori and J. Malik. Recovering 3D human body configurations using shape contexts. TPAMI, 28(7):1052–1062, July 2006
16. L. F. Bo, C. Sminchisescu, A. Kanaujia, and D. N. Metaxas. Fast algorithms for large scale conditional 3D prediction. In CVPR, pages 1–8, 2008
17. G. Rogez and C. Schmid. Mocap-guided data augmentation for 3D pose estimation in the wild. In NIPS, 2016
18. G. Pavlakos, X. Zhou, K. G. Derpanis, and K. Daniilidis.Coarse-to-fine volumetric prediction for single-image 3D human pose. In CVPR, 2017.
19. D. Eigen and R. Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In ICCV, 2015.
20. F. Liu, C. Shen, and G. Lin. Deep convolutional neural fields for depth estimation from a single image. In CVPR, pages 5162–5170. IEEE Computer Society, 2015.
21. .A. Popa, M. Zanfir, and C. Sminchisescu. Deep Multitask Architecture for Integrated 2D and 3D Human Sensing. In CVPR, 2017
22. . F. Bogo, A. Kanazawa, C. Lassner, P. Gehler, J. Romero,and M. J. Black. Keep it SMPL: Automatic estimation of 3d human pose and shape from a single image. In ECCV, 2016.], 55. X. Zhou, M. Zhu, S. Leonardos, and K. Daniilidis. Sparse representation for 3d shape estimation: A convex relaxation approach. TPAMI, 99(1), 2016
23. X. Zhou, M. Zhu, S. Leonardos, K. G. Derpanis, and K. Daniilidis. Sparseness meets deepness: 3d human pose estimation from monocular video. In CVPR, 2016
24. X. Zhou, X. Sun, W. Zhang, S. Liang, and Y. Wei. Deep kinematic pose regression. In ECCV Workshops, 2016
25. Types of Body Movements Edited and Revised by Lindsay M. Biga, Sierra Dawson, Amy Harwell, Robin Hopkins, Joel Kaufmann, Mike LeMaster, Philip Matern, Katie Morrison-Graham, Devon Quick, Jon Runyeon Art edited and created by Leeah Whittier
26. Judge, J. O., Ounpuu, S., & Davis, R. B., 3rd. (1996). Effects of age on the biomechanics and physiology of gait. Clin Geriatr Med, 12(4), 659-678
27. Teixeira-Salmela, L. F., Nadeau, S., McBride, I., & Olney, S. J. (2001).Effects of muscle strengthening and physical conditioning training on temporal, kinematic and kinetic variables during gait in chronic stroke survivors. J Rehabil Med, 33(2), 53-60.
28. DiBenedetto, M., Innes, K. E., Taylor, A. G., Rodeheaver, P. F., Boxer, J. A., Wright, H. J., & Kerrigan,
D. C. (2005). Effect of a gentle Iyengar yoga program on gait in the elderly: An exploratory study. Archives of Physical Medicine and Rehabilitation, 86(9), 1830-1837
29. Roland, K. P., Jakobi, J. M., & Jones, G. R. (2011). Does Yoga Engender Fitness in Older Adults? A Critical Review. Journal of Aging & Physical Activity, 19(1), 62-79.
30. Tran, M. D., Holly, R. G., Lashbrook, J., & Amsterdam, E. A. (2001). Effects of Hatha Yoga Practice on the Health-Related Aspects of Physical Fitness. PrevCardiol, 4(4), 165-170.
31. Omkar, S. N., Mour, M., & Das, D. (2011). A mathematical model of effects on specific joints during practice of the Sun Salutation - A sequence of yoga postures. Journal of Bodywork and Movement Therapie, 15(2), 201-208.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
