Literature DB >> 9391869

Forces of individual cat ankle extensor muscles during locomotion predicted using static optimization.

B I Prilutsky1, W Herzog, T L Allinger.   

Abstract

In order to test the principles of the control of synergistic muscles that were proposed in the literature, forces of cat soleus (SO), gastrocnemius (GA), and plantaris (PL) measured during locomotion were compared with the corresponding forces predicted using different optimization criteria. Forces of cat SO, GA, and PL, and the corresponding cat kinematics were recorded simultaneously using E-shaped force transducers and high-speed video, respectively. Measurements were obtained from three cats walking and trotting on a treadmill at five nominal speeds ranging from 0.4 to 1.8 m s-1. Muscle forces were predicted using static optimization and a musculoskeletal model of the cat hindlimb consisting of three segments (foot, shank, and thigh) and three muscles (SO, GA, and PL). Six optimization criteria which had been proposed in the literature were tested. Linear criteria based on the principles of minimum muscle force and stress predicted forces during the stance phase with an average normalized error of 59-322%. Three other criteria--minimization of the sum of the relative muscle forces squared, minimization of the sum of the muscle stresses cubed, and minimization of the upper bound for all of the muscle stresses-showed a better performance: (i) the average error was 43-119% and (ii) the correlation coefficient calculated between the predicted and actual forces exceeded 0.9 for all three muscles. A criterion that was based on the principle of minimum fatigue and accounted for the percentage of slow-twitch fibers in the muscles, had the lowest average error (26-52%). The high correlation (0.97-0.99) between the measured forces and forces predicted by using the minimum fatigue criterion suggested that force sharing among SO, GA, and PL during cat locomotion may be the same for a given set of joint moments and muscle moment arms. It was concluded that static optimization with the appropriate criterion can predict muscle forces adequately for specific movement conditions.

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Year:  1997        PMID: 9391869     DOI: 10.1016/s0021-9290(97)00068-7

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  9 in total

Review 1.  Optimization-based models of muscle coordination.

Authors:  Boris I Prilutsky; Vladimir M Zatsiorsky
Journal:  Exerc Sport Sci Rev       Date:  2002-01       Impact factor: 6.230

2.  Biomechanical capabilities influence postural control strategies in the cat hindlimb.

Authors:  J Lucas McKay; Thomas J Burkholder; Lena H Ting
Journal:  J Biomech       Date:  2006-12-06       Impact factor: 2.712

3.  Evaluation of a particle swarm algorithm for biomechanical optimization.

Authors:  Jaco F Schutte; Byung-Il Koh; Jeffrey A Reinbolt; Raphael T Haftka; Alan D George; Benjamin J Fregly
Journal:  J Biomech Eng       Date:  2005-06       Impact factor: 2.097

Review 4.  Functional and architectural complexity within and between muscles: regional variation and intermuscular force transmission.

Authors:  Timothy E Higham; Andrew A Biewener
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2011-05-27       Impact factor: 6.237

Review 5.  Review and perspective: neuromechanical considerations for predicting muscle activation patterns for movement.

Authors:  Lena H Ting; Stacie A Chvatal; Seyed A Safavynia; J Lucas McKay
Journal:  Int J Numer Method Biomed Eng       Date:  2012-05-16       Impact factor: 2.747

6.  Analytical and numerical analysis of inverse optimization problems: conditions of uniqueness and computational methods.

Authors:  Alexander V Terekhov; Vladimir M Zatsiorsky
Journal:  Biol Cybern       Date:  2011-02-11       Impact factor: 2.086

7.  Defining feasible bounds on muscle activation in a redundant biomechanical task: practical implications of redundancy.

Authors:  M Hongchul Sohn; J Lucas McKay; Lena H Ting
Journal:  J Biomech       Date:  2013-03-12       Impact factor: 2.712

8.  Optimization of muscle activity for task-level goals predicts complex changes in limb forces across biomechanical contexts.

Authors:  J Lucas McKay; Lena H Ting
Journal:  PLoS Comput Biol       Date:  2012-04-12       Impact factor: 4.475

9.  The cost of leg forces in bipedal locomotion: a simple optimization study.

Authors:  John R Rebula; Arthur D Kuo
Journal:  PLoS One       Date:  2015-02-23       Impact factor: 3.240

  9 in total

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