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Motion Capture for Clinical Purposes, an Approach Using PrimeSense Sensors

  • Gabriel Sanmartín
  • Julián Flores
  • Pablo Arias
  • Javier Cudeiro
  • Roi Méndez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7378)

Abstract

Virtual Reality (VR) is the computer recreation of simulated environments that create on the user a sense of physical presence on them. VR provides the advantages of being highly flexible and controllable, allowing experts to generate the optimal conditions for any given test and isolating any desired variables in the course of an experiment. An important characteristic of VR is that it allows interaction within the virtual world. Motion capture is one of the most popular technologies, because it contributes to creating in the subject the required sense of presence. There are several methods to incorporate these techniques into VR system, with the challenge of them not being too invasive. We propose a method using PrimeSense sensors and several well-known computer vision techniques to build a low-cost mocap system that has proven to be valid for clinical needs, in its application as a support therapy for Parkinson’s disease (PD) patients.

Keywords

motion capture prime sense Kinect Parkinson inverse kinematics virtual reality 

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References

  1. 1.
    Wang, L.-C.T., Chen, C.C.: A combined optimization method for solving the inverse kinematics problems of mechanical manipulators. IEEE Transactions on Robotics and Automation 7(4), 489–499 (1991)CrossRefGoogle Scholar
  2. 2.
    Arias, P., Robles-García, V., Sanmartín, G., Flores, J., Cudeiro, J.: Virtual Reality as a Tool for Evaluation of Repetitive Rhythmic Movements in the Elderly and Parkinson’s Disease Patients. PLoS ONE 7(1), e30021, doi:10.1371/journal.pone.0030021Google Scholar
  3. 3.
    Bradski, G.R.: Computer Vision Face Tracking for Use in a Perceptual User Interface. Intel Technology Journal, Q2 1998 (1998)Google Scholar
  4. 4.
    Zhang, C., Qiao, Y., Fallon, E., Xu, C.: An improved CamShift algorithm for target tracking in video surveillanceGoogle Scholar
  5. 5.
    Xiangyu, W., Xiujuan, L.: The Study of Moving Target Tracking Based on Kalman-CamShift in the VideoGoogle Scholar
  6. 6.
    Dae-Sik, J., Gye-Young, K., Choi-Hyung, I.: Kalman Filter incorporated model updating for real-time tracking. In: Proc. 1996 IEEE TENCON, Digital Signal Processing Applications, pp. 878–882 (1996)Google Scholar
  7. 7.
    Varona, J., Buades, J.M., Perales, F.: Hands and face tracking for VR applications. Computers & Graphics 29(2), 179–187 (2005)CrossRefGoogle Scholar
  8. 8.
    Manresa, C., Varona, J., Mas, R., Perales, F.: Hand tracking and Gesture Recognition for Human-Computer InteractionGoogle Scholar
  9. 9.
  10. 10.
    Konolige, K., Mihelich, P.: Technical description of Kinect calibration, http://www.ros.org/wiki/kinect_calibration/technical
  11. 11.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, Boston (1990)zbMATHGoogle Scholar
  12. 12.
    Cheng, Y.: Mean shift, mode seeking and clustering. IEEE Trans. Pattern Anal. Machine Intell. 17, 790–799 (1995)CrossRefGoogle Scholar
  13. 13.
    Hodges, L., Kooper, R., Orothbaum, B., Opddyke, D.: Virtual environments for treating the fear of heights. IEEE Computer, 28–84 (1995)Google Scholar
  14. 14.
    Carlin, A.S., Hoffman, H.G., Weghorst, S.: Virtual reality and tactile augmentation in the treatment of spider phobia: A case report. Behaviour Research and Therapy 35(2), 153–158 (1997)CrossRefGoogle Scholar
  15. 15.
    Pertaub, D.P., Slater, M., Barker, C.: An experiment on public speaking anxiety in response to three different types of virtual audience. Presence-Teleoperators and Virtual Environments 11(1), 68–78 (2002)CrossRefGoogle Scholar
  16. 16.
    Difede, J., Hoffman, H., Jaysinghe, N.: Innovative use of virtual reality technology in the treatment of PTSD in the aftermath of September 11. Psychiatric Services 53(9), 1083–1085 (2002)CrossRefGoogle Scholar
  17. 17.
    Granger, C.V., Hamilton, B.B., Sherwin, F.G.: Guide for the use of uniform data set for medical rehabilitation. Uniform Data System for Medical Rehabilitation. Buvalo General Hospital, New YorkGoogle Scholar
  18. 18.
    Bennett, K.M., Castiello, U.: Reach to grasp: changes with age. J. Gerontol. 49(1), 1–7 (1994)Google Scholar
  19. 19.
    Carnahan, H., Vandervoort, A.A., Swanson, L.R.: The influence of aging and target motion on the control of prehension. Exp. Aging. Res. 24(3), 289–306Google Scholar
  20. 20.
    Majsak, M.J., Kaminski, T., Gentile, A.M., Flanagan, J.R.: The reaching movements of patients with Parkinson’s disease under self-determined maximal speed and visually cued conditions. Brain 121(Pt 4), 755–766Google Scholar
  21. 21.
    Negrotti, A., Secchi, C., Gentilucci, M.: Effects of disease progression and L-dopa therapy on the control of reaching-grasping in Parkinson’s disease. Neuropsychologia 43(3), 450–459Google Scholar
  22. 22.
    Jeannerod, M.: Intersegmental coordination during reaching at natural visual objects. In: Long, J., Baddeley, A. (eds.) Attention and Performance IX, pp. 153–168. Lawrence Erlbaum, HillsdaleGoogle Scholar
  23. 23.
  24. 24.
  25. 25.
  26. 26.
    Neuroscience and Motor Control Group (Neurocom), http://www.udc.es/dep/medicina/neurocom.htm
  27. 27.
    OpenCV. Open Source Computer Vision, http://opencv.willowgarage.com/

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gabriel Sanmartín
    • 1
  • Julián Flores
    • 1
  • Pablo Arias
    • 2
  • Javier Cudeiro
    • 1
  • Roi Méndez
    • 1
  1. 1.Instituto de Investigacións TecnolóxicasCoGRADESantiago de CompostelaSpain
  2. 2.Neuroscience and Motor Control Group (NEUROcom), Department of Medicine-INEF GaliciaUniversity of A CoruñaSpain

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