Direct Comparison of Muscle Force Predictions Using Linear and Nonlinear Programming

[+] Author and Article Information
D. R. Pedersen, R. A. Brand, C. Cheng

Orthopaedic Biomechanics Laboratory, The University of Iowa, Iowa City, Iowa 52242

J. S. Arora

Applied-Optimal Design Laboratory, Civil and Environmental Engineering, The University of Iowa, Iowa City, Iowa 52242

J Biomech Eng 109(3), 192-199 (Aug 01, 1987) (8 pages) doi:10.1115/1.3138669 History: Received July 28, 1986; Revised May 27, 1987; Online June 12, 2009


Estimating forces in muscles and joints during locomotion requires formulations consistent with available methods of solving the indeterminate problem. Direct comparisons of results between differing optimization methods proposed in the literature have been difficult owing to widely varying model formulations, algorithms, input data, and other factors. We present an application of a new optimization program which includes linear and nonlinear techniques allowing a variety of cost functions and greater flexibility in problem formulation. Unified solution methods such as the one demonstrated here, offer direct evaluations of such factors as optimization criteria and constraints. This unified method demonstrates that nonlinear formulations (of the sort reported) allow more synergistic activity and in contrast to linear formulations, allow antagonistic activity. Concurrence of EMG activity and predicted forces is better with nonlinear predictions than linear predictions. The prediction of synergistic and antagonistic activity expectedly leads to higher joint force predictions. Relaxation of the requirement that muscles resolve the entire intersegmental moment maintains muscle synergism in the nonlinear formulation while relieving muscle antagonism and reducing the predicted joint contact force. Such unified methods allow more possibilities for exploring new optimization formulations, and in comparing the solutions to previously reported formulations.

Copyright © 1987 by ASME
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