Literature DB >> 21840224

An EMG-driven model applied for predicting metabolic energy consumption during movement.

Maria Cristina Bisi1, Rita Stagni, Han Houdijk, Gianni Gnudi.   

Abstract

The relationship between mechanical work and metabolic energy cost during movement is not yet clear. Many studies demonstrated the utility of forward-dynamic musculoskeletal models combined with experimental data to address such question. The aim of this study was to evaluate the applicability of a muscle energy expenditure model at whole body level, using an EMG-driven approach. Four participants performed a 5-min squat exercise on unilateral leg press at two different frequencies and two load levels. Data collected were kinematics, EMG, forces and moments under the foot and gas-exchange data. This same task was simulated using a musculoskeletal model, which took EMG and kinematics as inputs and gave muscle forces and muscle energetics as outputs. Model parameters were taken from literature, but maximal isometric muscle force was optimized in order to match predicted joint moments with measured ones. Energy rates predicted by the model were compared with energy consumption measured by the gas-exchange data. Model results on metabolic energy consumption were close to the values obtained through indirect calorimetry. At the higher frequency level, the model underestimated measured energy consumption. This underestimation can be explained with an increase in energy consumption of the non-muscular mass with movement velocity. In conclusion, results obtained in comparing model predictions with experimental data were promising. More research is needed to evaluate this way of computing mechanical and metabolic work.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21840224     DOI: 10.1016/j.jelekin.2011.07.003

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  4 in total

1.  Time flies when you are in a groove: using entrainment to mechanical resonance to teach a desired movement distorts the perception of the movement's timing.

Authors:  Daniel K Zondervan; Jaime E Duarte; Justin B Rowe; David J Reinkensmeyer
Journal:  Exp Brain Res       Date:  2014-01-08       Impact factor: 1.972

2.  A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives.

Authors:  Massimo Sartori; Leonardo Gizzi; David G Lloyd; Dario Farina
Journal:  Front Comput Neurosci       Date:  2013-06-26       Impact factor: 2.380

3.  A fair and EMG-validated comparison of recruitment criteria, musculotendon models and muscle coordination strategies, for the inverse-dynamics based optimization of muscle forces during gait.

Authors:  Florian Michaud; Mario Lamas; Urbano Lugrís; Javier Cuadrado
Journal:  J Neuroeng Rehabil       Date:  2021-01-28       Impact factor: 4.262

Review 4.  Robot-assisted assessment of muscle strength.

Authors:  Marco Toigo; Martin Flück; Robert Riener; Verena Klamroth-Marganska
Journal:  J Neuroeng Rehabil       Date:  2017-10-11       Impact factor: 4.262

  4 in total

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