Optimization algorithm performance in determining optimal controls in human movement analyses.
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.
Neptune
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Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Canada.
D000465:Algorithms; D015982:Bias; D001642:Bicycling / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008297:Male; D009010:Monte Carlo Method; D009068:Movement / Q000502:physiology*; D015999:Multivariate Analysis; D009142:Musculoskeletal Physiological Phenomena; D016059:Range of Motion, Articular; D015203:Reproducibility of Results