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Hand Detection and Tracking Using the Skeleton of the Blob for Medical Rehabilitation Applications

  • Pedro Gil-Jiménez
  • Beatriz Losilla-López
  • Rafael Torres-Cueco
  • Aurélio Campilho
  • Roberto López-Sastre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)

Abstract

This article presents an image processing application for hand detection and tracking using the 4-connected skeleton of the segmentation mask. The system has been designed to be used with techniques of virtual reality to develop an interactive application for phantom limb pain reduction in therapeutic treatments.

One of the major contributions is the design of a fast and accurate skeleton extractor, that has proven to be faster than those available in the literature. The skeleton allows the system to precisely detect the position of all the interest points of the hand (namely the fingers and the hand center).

The system, composed of both the hand detector and tracker, and the virtual reality application, can work in real-time, allowing the patient to watch the virtual image of his own hand on a screen.

Keywords

hand detection and tracking blob skeleton virtual reality phantom limb pain reduction 

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References

  1. 1.
    Breuer, P., Eckes, C., Müller, S.: Hand Gesture Recognition with a Novel IR Time-of-Flight Range Camera–A Pilot Study. In: Gagalowicz, A., Philips, W. (eds.) MIRAGE 2007. LNCS, vol. 4418, pp. 247–260. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Chang, C., Lin, C.: LIBSVM: a library for support vector machines (2001), software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  3. 3.
    Cole, J., Crowle, S., Austwick, G., Slater, D.H.: Exploratory findings with virtual reality for phantom limb pain; from stump motion to agency and analgesia. Disability and Rehabilitation 31(10), 846–854 (2009)CrossRefGoogle Scholar
  4. 4.
    Elgammal, A., Muang, C., Hu, D.: Skin detection - a short tutorial. In: Encyclopedia of Biometrics (2009)Google Scholar
  5. 5.
    Gómez-Moreno, H., Maldonado-Bascón, S., Gil-Jiménez, P., Lafuente-Arroyo, S.: Goal evaluation of segmentation algorithms for traffic sign recognition, vol. 11(4), pp. 917–930 (July 2010)Google Scholar
  6. 6.
    González, R., Woods, R.: Digital Image Processing. Addison-Wesley (1993)Google Scholar
  7. 7.
    von Hardenberg, C., Bérard, F.: Bare-hand human-computer interaction. In: Proceedings of the 2001 Workshop on Perceptive user Interfaces, PUI 2001, pp. 1–8 (2001)Google Scholar
  8. 8.
    Hsieh, C.C., Liou, D.H., Lee, D.: A real time hand gesture recognition system using motion history image. In: 2010 2nd International Conference on Signal Processing Systems (ICSPS), vol. 2, pp. V2-394–V2-398 (July 2010)Google Scholar
  9. 9.
    Imai, A., Shimada, N., Shirai, Y.: 3-d hand posture recognition by training contour variation. In: Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 895–900 (May 2004)Google Scholar
  10. 10.
    Isaacs, J., Foo, J.: Hand pose estimation for american sign language recognition. In: Southern Symposium on System Theory, pp. 132–136 (2004)Google Scholar
  11. 11.
    Lakshmi, J.K., Punithavalli, M.: A survey on skeletons in digital image processing. Digital Image Processing, 260 – 269 (2009)Google Scholar
  12. 12.
    Letessier, J., Bérard, F.: Visual tracking of bare fingers for interactive surfaces. In: Proceedings of the 17th Annual ACM Symposium on User Interface Software and Technology, UIST 2004, pp. 119–122. ACM (2004)Google Scholar
  13. 13.
    Lewis, J.S., Kersten, P., McCabe, C.S., McPherson, K.M., Blake, D.R.: Body perception disturbance: A contribution to pain in complex regional pain syndrome. In: CRPS (2007)Google Scholar
  14. 14.
    McCabe, C.S., Haigh, R.C., Ring, E.F., Halligan, P.W., Wall, P.D., Blake, D.R.: A controlled pilot study of the utility of mirror visual feedback in the treatment of complex regional pain syndrome (type 1). Rheumatology 42(1), 97–101 (2003)CrossRefGoogle Scholar
  15. 15.
    Murray, C.D., Patchick, E., Pettifer, S., Howard, T., Caillette, F., Kulkarni, J., Bamford, C.: Investigating the efficacy of a virtual mirror box in treating phantom limb pain in a sample of chronic sufferers. Disabil Human Dev. 5(3), 227–234 (2006)CrossRefGoogle Scholar
  16. 16.
    Nope, R., Sandra, E., Humberto Loaiza, C., Eduardo Caicedo, B.: Estudio omparativo de técnicas para el reconocimiento de gestos por visión artificial. Avances en Sistemas e Informtica 5(3) (2009)Google Scholar
  17. 17.
    Oikonomidis, I., Nikolaos, K., Argyros, A.A.: Efficient model-based 3d tracking of hand articulations using kinect. In: Tracking Hand Articulations using Kinect (2011)Google Scholar
  18. 18.
    Pugeault, N., Bowden, R.: Spelling it out: Real-time ASL fingerspelling recognition. In: IEEE Workshop on Consumer Depth Cameras for Computer Vision (2011)Google Scholar
  19. 19.
    Ren, Z., Meng, J., Yuan, J., Zhang, Z.: Robust hand gesture recognition with kinect sensor. In: Proceedings of the 19th ACM International Conference on Multimedia, MM 2011, pp. 759–760. ACM (2011)Google Scholar
  20. 20.
    Yoruk, E., Konukoglu, E., Sankur, B., Darbon, J.: Shape-based hand recognition. IEEE Transactions on Image Processing 15(7), 1803–1815 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pedro Gil-Jiménez
    • 1
  • Beatriz Losilla-López
    • 1
  • Rafael Torres-Cueco
    • 2
  • Aurélio Campilho
    • 3
  • Roberto López-Sastre
    • 1
  1. 1.Universidad de AlcaláAlcalá de HenaresSpain
  2. 2.Universidad de ValenciaValenciaSpain
  3. 3.INEB - Instituto de Engenharia Biomédica, Faculdade de EngenhariaUniversidade do PortoPortugal

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