• DocumentCode
    2375453
  • Title

    Unsupervised model generation for motion monitoring

  • Author

    Weber, Markus ; Bleser, Gabriele ; Hendeby, Gustaf ; Reiss, Attila ; Stricker, Didier

  • Author_Institution
    Augmented Vision, German Res. Center for Artificial Intell. (DFKI) GmbH, Kaiserslautern, Germany
  • fYear
    2011
  • fDate
    9-12 Oct. 2011
  • Firstpage
    51
  • Lastpage
    54
  • Abstract
    This paper addresses two fundamental requirements of full body motion monitoring: (a) the ability to sense the input of the user and (b) the means to interpret the captured input. Appropriate technology in both areas is required for an interactive virtual reality system to provide feedback in a useful and natural way. This paper combines technologies for both areas: It develops a sensor fusion approach for capturing user input based on miniature on-body inertial and magnetic motion sensors. Furthermore, it presents work in progress to automatically generate models for motion patterns from the captured input. The technology is then used and evaluated in the context of a personalized virtual rehabilitation trainer application.
  • Keywords
    medical computing; patient rehabilitation; sensor fusion; user interfaces; virtual reality; full body motion monitoring; magnetic motion sensor; on-body inertial sensor; personalized virtual rehabilitation trainer application; sensor fusion approach; unsupervised model generation; user input; virtual reality system; Biological system modeling; Hidden Markov models; Joints; Monitoring; Motion segmentation; Training; Viterbi algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    1062-922X
  • Print_ISBN
    978-1-4577-0652-3
  • Type

    conf

  • DOI
    10.1109/ICSMC.2011.6083641
  • Filename
    6083641