• DocumentCode
    1587173
  • Title

    A Method for Identification of Electrically Stimulated Muscle

  • Author

    Farahat, Waleed ; Her, Hugh

  • Author_Institution
    Dept. of Mech. Eng., MIT, Cambridge, MA
  • fYear
    2005
  • fDate
    6/27/1905 12:00:00 AM
  • Firstpage
    6225
  • Lastpage
    6228
  • Abstract
    We present a model structure and a method for identifying the dynamics of electrically stimulated muscle. The model structure is sufficiently rich to describe a wide set of muscle behavior. It consists of (i) an input static nonlinearity representing the muscle´s recruitment properties, (ii) a linear dynamical system representing the contraction dynamics, (iii) an output static nonlinearity representing generalized force-length and force-velocity relationships, and (iv) prefilters for the mechanical input that capture impedance and history dependence properties of the muscle. It is assumed that each of the subsystems is linearly parameterized. We present parameter estimation methods, and verify via simulation successful convergence of the estimates to their true values with small variances
  • Keywords
    bioelectric phenomena; biomechanics; electric impedance; force; muscle; parameter estimation; physiological models; contraction dynamics; electrically stimulated muscle; force-length relationships; force-velocity relationships; history dependence properties; impedance; input static nonlinearity; linear dynamical system; muscle recruitment properties; output static nonlinearity; parameter estimation methods; prefilters; Biological system modeling; Convergence; Fatigue; History; Impedance; Mechanical factors; Muscles; Nonlinear dynamical systems; Parameter estimation; Recruitment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
  • Conference_Location
    Shanghai
  • Print_ISBN
    0-7803-8741-4
  • Type

    conf

  • DOI
    10.1109/IEMBS.2005.1615918
  • Filename
    1615918