TY - GEN

T1 - Modeling and identification of human musculoskeletal walking system

AU - Zhang, Li Qun

AU - Shiavi, Richard

AU - Wilkes, Mitchell

PY - 1990

Y1 - 1990

N2 - Several methods are tested to identify the human musculoskeletal system both as a linear and nonlinear system. For the linear system approach, a MIMO (multiinput, multioutput) ARX (autoregressive with exogeneous inputs) model is first tested to get a rough estimation of the system structure and parameters. A general linear input-output MIMO model is then developed, and parameters are estimated by means of the prediction error identification method. Since the complex human musculoskeletal system is almost certainly sure a nonlinear system, nonlinear system identification is applied and polynomials are used to approximate the nonlinear system functions. For such a MIMO nonlinear system, the parameters to be estimated will number in the thousands or even millions, depending on the polynomial degrees used and the maximum orders of delays. To overcome such numerical difficulties, a forward-regression orthogonal method is used to select only the most significant terms and estimate the corresponding parameters.

AB - Several methods are tested to identify the human musculoskeletal system both as a linear and nonlinear system. For the linear system approach, a MIMO (multiinput, multioutput) ARX (autoregressive with exogeneous inputs) model is first tested to get a rough estimation of the system structure and parameters. A general linear input-output MIMO model is then developed, and parameters are estimated by means of the prediction error identification method. Since the complex human musculoskeletal system is almost certainly sure a nonlinear system, nonlinear system identification is applied and polynomials are used to approximate the nonlinear system functions. For such a MIMO nonlinear system, the parameters to be estimated will number in the thousands or even millions, depending on the polynomial degrees used and the maximum orders of delays. To overcome such numerical difficulties, a forward-regression orthogonal method is used to select only the most significant terms and estimate the corresponding parameters.

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M3 - Conference contribution

AN - SCOPUS:0025590456

SN - 0818620382

T3 - Proceedings of the Annual Southeastern Symposium on System Theory

SP - 146

EP - 150

BT - Proceedings of the Annual Southeastern Symposium on System Theory

PB - Publ by IEEE

T2 - Proceedings of the 22nd Southeastern Symposium on System Theory

Y2 - 11 March 1990 through 13 March 1990

ER -