• DocumentCode
    3085364
  • Title

    Support Vector Machines for Anatomical Joint Constraint Modelling

  • Author

    Jenkins, A. Glenn L ; Dacey, Michael

  • Author_Institution
    Sch. of Appl. Comput., Swansea Metropolitan Univ., Swansea
  • fYear
    2009
  • fDate
    25-27 March 2009
  • Firstpage
    186
  • Lastpage
    190
  • Abstract
    The accurate simulation of anatomical joint models is becoming increasingly important for both realistic animation and diagnostic medical applications. Recent models have exploited unit quaternions to eliminate singularities when modeling orientations between limbs at a joint. This has led to the development of quaternion based joint constraint validation and correction methods. In this paper a novel method for implicitly modeling unit quaternion joint constraints using Support Vector Machines (SVMs) is proposed which attempts to address the limitations of current constraint validation approaches. Initial results show that the resulting SVMs are capable of modeling regular spherical constraints on the rotation of the limb.
  • Keywords
    computer animation; medical diagnostic computing; physiological models; support vector machines; anatomical joint constraint modelling; diagnostic medical application; quaternion based joint constraint correction; quaternion based joint constraint validation; realistic animation; support vector machine; unit quaternion joint constraint; Animation; Computational modeling; Computer simulation; Equations; Joints; Medical services; Medical simulation; Quaternions; Support vector machine classification; Support vector machines; Support Vector Machine; anatomical joint constraint; joint constraint; quaternion;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Modelling and Simulation, 2009. UKSIM '09. 11th International Conference on
  • Conference_Location
    Cambridge
  • Print_ISBN
    978-1-4244-3771-9
  • Electronic_ISBN
    978-0-7695-3593-7
  • Type

    conf

  • DOI
    10.1109/UKSIM.2009.21
  • Filename
    4809760