Construction principles of computer systems for remote training based on the analysis of the video stream

Authors

  • Гуі Кіонг Нгуєн Odessa State Polytechnical University Shevchenko Av.,1, Odessa, , Ukraine, 65044, Ukraine
  • Віктор Олексійович Болтьонков Odessa State Polytechnical University Shevchenko Av.,1, Odessa, , Ukraine, 65044, Ukraine https://orcid.org/0000-0001-5216-5534
  • Дмитро Вадимович Малявін Odessa State Polytechnical University Shevchenko Av.,1, Odessa, , Ukraine, 65044, Ukraine

DOI:

https://doi.org/10.15587/1729-4061.2014.28555

Keywords:

remote training, model of human movement, characteristic points, skeletonization, similarity degree of movements

Abstract

Basic construction principles of the remote training system were investigated. Operation of the system is based on the fact that the user tries to reproduce as accurately as repetitive movements, performed by the instructor. Algorithms for body movement image processing in the video stream were chosen so that the system was accessible to a wide range of users with home webcam and midrange computer. Point kinematic model of the human body movement was developed. The characteristic points of the human body in the video stream frames are determined based on the image skeletonization. According to the video stream data, for each characteristic point, its position, velocity and acceleration are calculated. Based on these data, a matrix of kinematic parameters for training and user movements is constructed. Quantitative comparison of two matrices is carried out using the Chebyshev and cosine similarity measures of vectors. Based on a comparison of the difference measures of vectors, recommendations are given to the user for correction of his movements. A prototype of the system was implemented as a software project. System testing has shown the correctness of its construction principles. Remote training system can be used in telemedicine for the rehabilitation of patients with musculoskeletal disorders, as well as remote sports training.

Author Biographies

Гуі Кіонг Нгуєн, Odessa State Polytechnical University Shevchenko Av.,1, Odessa, , Ukraine, 65044

PhD student

The department of information systems

Віктор Олексійович Болтьонков, Odessa State Polytechnical University Shevchenko Av.,1, Odessa, , Ukraine, 65044

Candidate of Technical Sciences, Assisiated Professor

The department of information systems

Дмитро Вадимович Малявін, Odessa State Polytechnical University Shevchenko Av.,1, Odessa, , Ukraine, 65044

Master student

The department of information systems

References

  1. Aggarwal, J. K., Cai, Q. (1997). Human motion analysis: a review. Proceedings IEEE Nonrigid and Articulated Motion Workshop. doi:10.1109/namw.1997.609859
  3. Katayev, M. Yu. (2012). Opredeleniye i analiz dvigatelnoy aktivnosti postinsultnogo patsiyenta iz potoka izobrazheniy . Meditsinskaya іnformatika, 4 (34), 43–50.
  5. Nhuen, H. K. (2013). Primeneniye sistem kompyuternogo zreniya v zadachakh reabilitatsii patsiyentov s boleznyami oporno–dvigatelnogo apparata . Trudy MNPK «Sovremennyye informatsionnyye i elektronnyye tekhnologii SIET–2013», Odessa, Politekhperiodika, 53–54.
  7. Shingade, A., Ghotkar, A. (2014). Animation of 3D Human Model Using Markerless Motion Capture Applied To Sports. IJCGA, 4 (1), 27–39. doi:10.5121/ijcga.2014.4103
  9. Pawlik, P., Bublinski, Z. (2012). Visual Surveillance for Movement Rehabilitation. Image Processing & Communications, 17 (4), 173–178. doi:10.2478/v10248-012-0044-9
  11. Chang, Y.-J., Chen, S.-F., Huang, J.-D. (2011). A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in Developmental Disabilities, 32 (6), 2566–2570. doi:10.1016/j.ridd.2011.07.002
  13. Kurakin, A. (2012). A real time system for dynamic hand gesture recognition with a depth sensor. EUSIPCO-2012: Proceedings of the 20-th European Signal Processing Conference, 1975–1979.
  15. Zhang, Z., Seah, H. S., Quah, C. K., Sun, J. (2011). A markerless motion capture system with automatic subject-specific body model acquisition and robust pose tracking from 3D data. 2011 18th IEEE International Conference on Image Processing, 525–528. doi:10.1109/icip.2011.6116397
  17. Manikandan, К. Human object detection and tracking using background subtraction for sports applications (2013). International Journal of Advanced Research in Computer and Communication Engineering, 2 (10), 4077–4080.

  19. 10. Kannan, P. (2012). Development of human pose models for sports dynamics analysis using video image processing techniques. International Journal of Sports Science and Engineering, 11 (4), 232–238.

  21. Duchenne, O., Laptev, I., Sivic, J., Bach, F., Ponce, J. (2009). Automatic annotation of human actions in video. 2009 IEEE 12th International Conference on Computer Vision, 1491–1498. doi:10.1109/iccv.2009.5459279

  23. 12. Kotyuzhanskiy, L. A. (2013). Interfeys beskontaktnogo upravleniya. Fundamentalnyye issledovaniya, 4 (1), 44–48.

  25. Manikandan, К., Ramakrishnan, R. (2013). Human object detection and tracking using background subtraction for sports applications. International Journal of Advanced Research in Computer and Communication Engineering, 2 (10), 4077–4080.
  27. Chuang, C.-H., Hsieh, J.-W., Tsai, L.-W., Fan, K.-C. (2008). Human Action Recognition Using Star Templates and Delaunay Triangulation. 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing. doi:10.1109/iih-msp.2008.342
  29. Hsieh, J.-W., Chuang, C.-H., Chen, S.-Y., Chen, C.-C., Fan, K.-C. (2010). Segmentation of Human Body Parts Using Deformable Triangulation. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 40 (3), 596–610. doi:10.1109/tsmca.2010.2040272
  31. Morrison, P., Zou, J. J. (2007). Triangle refinement in a constrained Delaunay triangulation skeleton. Pattern Recognition, 40 (10), 2754–2765. doi:10.1016/j.patcog.2006.12.021
  33. Zatsiorskiy, V. M. (1981). Biomekhanika dvigatelnogo apparata cheloveka. Moscow: Fizkultura i sport, 143.
  35. Umnov, A. E. ( 2011). Analiticheskaya geometriya i lineynaya algebra. Moscow: MFTI, 544.
  37. Salnikov, I. I. (2009). Rastrovyye prostranstvenno–vremennyye signaly v sistemakh analiza izobrazheniy. Moscow: Fizmatlit, 248.
  39. Mestetskiy, L. M. (2009). Nepreryvnaya morfologiya binarnykh izobrazheniy: figury, skelety, tsirkulyary. Moscow: Fizmatlit, 286.
  41. Serra, J. (1982). Image analysis and mathematical morphology. London: Academic Press, 605.
  43. Pfaltz, J. L., Rosenfeld, A. (1967). Computer representation of planar regions by their skeletons. Communications of the ACM, 10 (2), 119–122. doi:10.1145/363067.363120
  45. Zhang, T. Y., Suen, C. Y. (1984). A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27 (3), 236–239. doi:10.1145/357994.358023
  47. Guo, Z., Hall, R. W. (1989). Parallel thinning with two-subiteration algorithms. Communications of the ACM, 32 (3), 359–373. doi:10.1145/62065.62074
  49. Stentiford, F. W. M., Mortimer, R. G. (1983). Some new heuristics for thinning binary handprinted characters for OCR. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13 (1), 81–84. doi:10.1109/tsmc.1983.6313034
  51. Pospelov, D. A. (1990). Iskusstvennyy intellekt. Modeli i metody: Spravochnik. Book 2. Moscow: Radio i svyaz, 304.

Downloads

Published

2014-10-24

How to Cite

Нгуєн, Г. К., Болтьонков, В. О., & Малявін, Д. В. (2014). Construction principles of computer systems for remote training based on the analysis of the video stream. Eastern-European Journal of Enterprise Technologies, 5(2(71), 25–33. https://doi.org/10.15587/1729-4061.2014.28555

Issue

Vol. 5 No. 2(71) (2014): Information technology. Industry control systems

Section

Information technology

License

Copyright (c) 2014 Віктор Олексійович Болтьонков, Гуі Кіонг Нгуєн, Дмитро Вадимович Малявін

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.