Literature DB >> 16439246

The relationship between two different mechanical cost functions and muscle oxygen consumption.

M Praagman1, E K J Chadwick, F C T van der Helm, H E J Veeger.   

Abstract

Inverse-dynamic models often use cost functions to solve the load-sharing problem. Although it is often assumed that energy is minimised, most cost functions are based on mechanically related measures like muscle force or stress. The aim of this study was to analyse the relationships of two cost functions with experimentally determined data on muscle energy consumption. Four subjects performed isometric contractions generating combinations of elbow flexion/extension and pro/supination moments. Muscle oxygen consumption (VO2) of the m. biceps breve, m. biceps longum, m. brachioradialis and m. triceps laterale was measured with near infrared spectroscopy. Both cost functions were implemented into an existing inverse-dynamic shoulder and elbow model and the individual cost values per muscle were calculated, normalised and subsequently compared to experimental VO2 values. The minimum stress cost function led to a good correspondence between VO2 and cost for the m. triceps laterale but for the flexor muscles cost was significantly lower. A newly proposed energy-related cost function showed, however, a far better correspondence. The inclusion of a linear term and muscle mass in the new criterion led model results to correspond better to experimental results. The energy-related cost function appeared to be a better measure for muscle energy consumption than the stress cost function and led to more realistic predictions of muscle activation.

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Year:  2006        PMID: 16439246     DOI: 10.1016/j.jbiomech.2004.11.034

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


  20 in total

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Journal:  Med Biol Eng Comput       Date:  2009-04-25       Impact factor: 2.602

2.  Arm load magnitude affects selective shoulder muscle activation.

Authors:  Frans Steenbrink; Carel G M Meskers; Bart van Vliet; Jorrit Slaman; H E J Veeger; Jurriaan H De Groot
Journal:  Med Biol Eng Comput       Date:  2009-04-07       Impact factor: 2.602

3.  Can muscle shortening alone, explain the energy cost of muscle contraction in vivo?

Authors:  Jared R Fletcher; Erik M Groves; Ted R Pfister; Brian R Macintosh
Journal:  Eur J Appl Physiol       Date:  2013-05-28       Impact factor: 3.078

Review 4.  Clinical applications of musculoskeletal modelling for the shoulder and upper limb.

Authors:  Bart Bolsterlee; Dirkjan H E J Veeger; Edward K Chadwick
Journal:  Med Biol Eng Comput       Date:  2013-07-20       Impact factor: 2.602

5.  Glenohumeral stability during a hand-positioning task in previously injured shoulders.

Authors:  James Marchi; Dimitra Blana; Edward K Chadwick
Journal:  Med Biol Eng Comput       Date:  2013-05-24       Impact factor: 2.602

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Authors:  Juan Gabriel Hincapie; Dimitra Blana; Edward K Chadwick; Robert F Kirsch
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2008-06       Impact factor: 3.802

7.  A musculoskeletal model of the upper extremity for use in the development of neuroprosthetic systems.

Authors:  Dimitra Blana; Juan G Hincapie; Edward K Chadwick; Robert F Kirsch
Journal:  J Biomech       Date:  2008-04-16       Impact factor: 2.712

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Journal:  J Anim Ecol       Date:  2019-06-27       Impact factor: 5.091

9.  Stochastic modelling of muscle recruitment during activity.

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Journal:  Interface Focus       Date:  2015-04-06       Impact factor: 3.906

10.  Feasibility of EMG-based neural network controller for an upper extremity neuroprosthesis.

Authors:  Juan Gabriel Hincapie; Robert F Kirsch
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2009-02       Impact factor: 3.802

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