Email this article A Distance Computer Vision Assisted Yoga Learning System
Abstract
Abstract—As computers and networks have been developed vigorously, distance learning could be integrated with computer vision techniques for the purpose of better learning effects. In this paper, we developed a distance yoga learning system for people to learn/play through the internet. The main point of the interactive learning system essentially consists in that the gesture performed by player, segmented by computer vision techniques, should possess the same silhouette for a given yoga posture. For better accuracy, the learning score is calculated by matching the distance transformation of the player silhouette with stored standard yoga posture. In the experiments, 23 postures were defined and six persons were invited to do each posture three times. About 86% of the difference between computer scores and the scores given by a yoga teacher falls within -2.5~2.5.
Keywords
References
[1] J. E. Deutsch, D. Robbins, J. Morrison, P. G. Bowlby,; Wii-based compared to standard of care balance and mobility rehabilitation for two individuals post-stroke, Virtual Rehabilitation International Conference, pp. 117 – 120, 2009.
http://dx.doi.org/10.1109/ICVR.2009.5174216
[2] Vuong, B.; McConville, K.; Use of recurrence quantification analysis in virtual reality training: A case study, Science and Technology for Humanity (TIC-STH), 2009 IEEE Toronto International Conference Digital pp. 849 – 854, Object 2009.
[3] J. Lai, E. Ziskind, F. Zheng, Y. Shao, C. Zhang, M. Zhang, N. Garg, S.Sobti, R. Wang, and A. Krishnamurthy, Distance Learning Technologies for Basic Education in Disadvantaged Areas Proceedings of the 8th Global Chinese Conference on Computers in Education, pp. 781-789, June 2004.
[4] Puranam, Muthukumar B.; Towards Full-Body Gesture Analysis and Recognition, The Graduate School University of Kentucky Master Thesis, 2005.
[5] H. Jiang, Z. N. Li, Mark S. Drew, Linear Programming for Matching in Human Body Gesture Recognition, IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 392–406, 2005.
http://dx.doi.org/10.1007/11564386_30
[6] “Natal”, http://www.microsoft.com/uk/wave/hardware-projectnatal.asp
[7] Jin-Quan Lin, The Behavior Analysis and Detection of Falling, Computer Science and Information Engineering Master Thesis, NCU, page 9, 2004.
[8] “Distance Transform”, http://homepages.inf.ed.ac.uk/rbf/HIPR2/metric.htm.
[9] Kuo-Ming Chen, 3-D Registration and Fusion of CT and MRI Images of Lumbar Vertebra.
[10] Engineering Science Master Thesis, NCKU, pp. 17-21, 2003.
[11] R. Brunelli, and T. Poggio, Template Matching: Matched Spatial Filters And Beyond, MIT AI Memo 1549, July 1995.
[12] Tzu-Thieh Chang-Chien, The FPGA Realization by Using CMAC for Vehicle License Plate Recognition, Industrial Education Master Thesis, NTNU, pp. 14-16, 2003.
[13] http://www.cognex.com/Main.aspx?langtype=1028
Full Text: PDF