Optimization algorithm performance in determining optimal controls in human movement analyses.

R R Neptune

doi:10.1115/1.2835111

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Optimization algorithm performance in determining optimal controls in human movement analyses.

R R Neptune

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Abstract

The objective of this study was to evaluate the performance of different multivariate optimization algorithms by solving a "tracking" problem using a forward dynamic model of pedaling. The tracking problem was defined as solving for the muscle controls (muscle stimulation onset, offset, and magnitude) that minimized the error between experimentally collected kinetic and kinematic data and the simulation results of pedaling at 90 rpm and 250 W. Three different algorithms were evaluated: a downhill simplex method, a gradient-based sequential quadratic programming algorithm, and a simulated annealing global optimization routine. The results showed that the simulated annealing algorithm performed for superior to the conventional routines by converging more rapidly and avoiding local minima.

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Algorithms Bias Bicycling Computer Simulation Humans Male Monte Carlo Method Movement Multivariate Analysis Musculoskeletal Physiological Phenomena Range of Motion, Articular Reproducibility of Results Journal Article Research Support, Non-U.S. Gov't

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BibTeX RIS

R R Neptune. 1999. Optimization algorithm performance in determining optimal controls in human movement analyses.. https://doi.org/10.1115/1.2835111

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