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The Development of a Human Gait Model With Predictive Capability and the Simulation of Able-Bodied Gait

[+] Author Affiliations
Jinming Sun

Whirlpool Corporation, Benton Harbor, MI

Shaoli Wu, Philip A. Voglewede

Marquette University, Milwaukee, WI

Paper No. DETC2015-47382, pp. V006T10A006; 9 pages
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 6: 11th International Conference on Multibody Systems, Nonlinear Dynamics, and Control
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5716-8
  • Copyright © 2015 by ASME


The development of current prostheses and orthoses typically follows a trial and error approach where the devices are designed based on experience, tried on human subjects and then redesigned iteratively. This design approach is costly, risky and time consuming. A predictive human gait model is desired such that prostheses can be virtually tested so that their performance can be predicted qualitatively, the cost can be reduced, and the risks can be minimized. The development of such a model is explained in this paper. The developed model includes two parts: a plant model which represents the forward dynamics of human gait and a controller which represents the central nervous system (CNS). The development of the plant model is explained in a different paper. This paper focuses on the control algorithm development and able-bodied gait simulation. The controller proposed in this paper utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize control input so that the error between them is minimal. The developed predictive human gait model was validated by simulating able-bodied human gait. The simulation results showed that the controller is able to simulate the kinematic output close to experimental data.

Copyright © 2015 by ASME
Topics: Simulation



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