Identification and validation of FES physiological musculoskeletal model in paraplegic subjects

The knowledge and prediction of the behavior of electrically activated muscles are important requisites for the movement restoration by FES in spinal cord injured subjects. The whole parameter’s identification of a physiological musculoskeletal model for FES is investigated in this work. The model represents the knee and its associated quadriceps muscle. The identification protocol is noninvasive and based on the in-vivo experiments on paraplegic subjects. The isometric and nonisometric data was obtained by stimulating the quadriceps muscles of 3 paraplegic subjects through surface electrodes. A cross validation has been carried out using nonisometric data set. The normalized RMS errors between the identified model and the measured knee response are presented for each subject.

[1]  A. Hill.  The heat of shortening and the dynamic constants of muscle , 1938 .

[2]  A. Huxley.  Muscle structure and theories of contraction. , 1957, Progress in biophysics and biophysical chemistry.

[3]  M. Hull,et al.  A method for determining lower extremity muscle-tendon lengths during flexion/extension movements. , 1990, Journal of biomechanics.

[4]  Henry M. Franken,et al.  Identification of quadriceps-shank dynamics using randomized interpulse interval stimulation , 1995 .

[5]  P. Leva.  Adjustments to Zatsiorsky-Seluyanov's segment inertia parameters. , 1996 .

[6]  J. Quintern,et al.  Physiologically Based Multi-Input Model of Muscle Activation , 1996 .

[7]  R B Stein,et al.  Estimating mechanical parameters of leg segments in individuals with and without physical disabilities. , 1996, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  Robert Riener,et al.  Neuroprosthetics: from Basic Research to Clinical Applications , 1996 .

[9]  R.B. Stein,et al.  Identification of electrically stimulated quadriceps muscles in paraplegic subjects , 1999, IEEE Transactions on Biomedical Engineering.

[10]  R. Riener,et al.  Model-based control of FES-induced single joint movements , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Fabio Previdi,et al.  Identification of black-box nonlinear models for lower limb movement control using functional electrical stimulation , 2002 .

[12]  Philippe Poignet,et al.  Mathematical muscle model for functional electrical stimulation control strategies , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[13]  Jörg Raisch,et al.  Online identification and nonlinear control of the electrically stimulated quadriceps muscle , 2005 .

[14]  E. Paul Zehr,et al.  A sigmoid function is the best fit for the ascending limb of the Hoffmann reflex recruitment curve , 2008, Experimental Brain Research.

[15]  Philippe Poignet,et al.  Nonlinear identification of skeletal muscle dynamics with sigma-point kalman filter for model-based FES , 2008, 2008 IEEE International Conference on Robotics and Automation.

[16]  Philippe Poignet,et al.  Optimal Functional Electrical Stimulation patterns synthesis for knee joint control , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.