Individual muscle force estimations using a non-linear optimal design

Herzog, Walter

Affiliations

¹Faculty of Physical Education, The University of Calgary, Calgary, Alta. (Canada)

Abstract and keywords

n order to estimate individual muscle forces during human movement a mathematically underdetermined system, representing the human musculoskeletal system, was solved using a non-linear optimization algorithm. The load sharing between muscles was made to depend upon the instantaneous contractile conditions of the muscles, a feature not incorporated in comparable non-linear optimal designs reported in the literature. The optimization algorithm was formulated as a convex system which guarantees a unique solution at the global minimum. The strengths and weaknesses of the present optimization algorithm were evaluated and compared to the classical algorithm reported by Crowninshield and Brand (J. Biomech., 14 (1981) 793–801).

Keywords: Muscle force estimation; Non-linear optimization; Muscle modelling

Links

DOI: 10.1016/0165-0270(87)90114-2

PubMed: 3682873

WoS: A1987K623200009

History

Received: 1986-09-15

Revised: 1987-02-15

Accepted: 1987-03-11

Cited Works (12)

Year	Entry
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Cited By (14)

Year	Entry
1990	Zajac FE, Winters JM. Modeling musculoskeletal movement systems: joint and body segmental dynamics, musculoskeletal actuation, and neuromuscular control. In: Winters JM, Woo SL-Y, eds. Multiple Muscle Systems: Biomechanics and Movement Organization. New York, NY: Springer-Verlag; 1990:121-148.
2001	Maganaris CN. Force–length characteristics of in vivo human skeletal muscle. Acta Physiol Scand. August 2001;172(4):279-285.
1990	Komi PV. Relevance of in vivo force measurements to human biomechanics. J Biomech. 1990;23(suppl 1):23-34.
2001	Rasmussen J, Damsgaard M, Voigt M. Muscle recruitment by the min/max criterion: a comparative numerical study. J Biomech. March 2001;34(3):409-415.
2001	Heller MO, Bergmann G, Deuretzbacher G, Dürselen L, Pohl M, Claes L, Haas NP, Duda GN. Musculo-skeletal loading conditions at the hip during walking and stair climbing. J Biomech. July 2001;34(7):883-893.
1992	Herzog W. Sensitivity of muscle force estimations to changes in muscle input parameters using nonlinear optimization approaches. J Biomech Eng. May 1992;114(2):267-268.
2006	Damsgaard M, Rasmussen J, Christensen ST, Surma E, de Zee M. Analysis of musculoskeletal systems in the AnyBody modeling system. Simul Model Pract Theory. November 2006;14(8):1100-1111.
2020	Honegger JD. Multiscale Modeling of the Lumbar Spine to Investigate Tissue-Level Load Transfer During Activities of Daily Living [PhD thesis]. Colorado School of Mines; 2020.
2000	Garceau P. Modélisation du contrôle neural des muscles du tronc [Master's thesis]. École polytechnique de Montréal; December 2000.
2005	Huynh A-M. Etude biomécanique des déformations rachidiennes dans la dystrophie musculaire de duchenne [PhD thesis]. École polytechnique de Montréal; 2005.
1989	Morlock M. A Generalized Three-Dimensional Six-Segment Model of the Ankle and the Foot [PhD thesis]. Calgary, AB: University of Calgary; December 1989.
1989	Read L. A Two-Dimensional Knee Joint Model Applied to Landing Movements in Alpine Skiing [Master's thesis]. Calgary, AB: University of Calgary; December 1989.
2002	Jinha A. Analyses of Muscle Force Predictions Based on Optimization [Master's thesis]. Calgary, AB: University of Calgary; May 2002.
1994	Nussbaum MA. Artificial Neural Network Models for Analysis of Lumbar Muscle Recruitment During Moderate Static Exertions [PhD thesis]. University of Michigan; 1994.