Literature DB >> 33732550

Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach.

Nicos Haralabidis1,2, Gil Serrancolí3, Steffi Colyer1,2, Ian Bezodis4, Aki Salo1,2,5, Dario Cazzola1,2.   

Abstract

Biomechanical simulation and modelling approaches have the possibility to make a meaningful impact within applied sports settings, such as sprinting. However, for this to be realised, such approaches must first undergo a thorough quantitative evaluation against experimental data. We developed a musculoskeletal modelling and simulation framework for sprinting, with the objective to evaluate its ability to reproduce experimental kinematics and kinetics data for different sprinting phases. This was achieved by performing a series of data-tracking calibration (individual and simultaneous) and validation simulations, that also featured the generation of dynamically consistent simulated outputs and the determination of foot-ground contact model parameters. The simulated values from the calibration simulations were found to be in close agreement with the corresponding experimental data, particularly for the kinematics (average root mean squared differences (RMSDs) less than 1.0° and 0.2 cm for the rotational and translational kinematics, respectively) and ground reaction force (highest average percentage RMSD of 8.1%). Minimal differences in tracking performance were observed when concurrently determining the foot-ground contact model parameters from each of the individual or simultaneous calibration simulations. The validation simulation yielded results that were comparable (RMSDs less than 1.0° and 0.3 cm for the rotational and translational kinematics, respectively) to those obtained from the calibration simulations. This study demonstrated the suitability of the proposed framework for performing future predictive simulations of sprinting, and gives confidence in its use to assess the cause-effect relationships of technique modification in relation to performance. Furthermore, this is the first study to provide dynamically consistent three-dimensional muscle-driven simulations of sprinting across different phases.
© 2021 Haralabidis et al.

Entities:  

Keywords:  Athletics; Human locomotion; Modelling; Nonlinear programming; Optimisation; Parameter estimation; Running; Sports biomechanics; Track and field

Year:  2021        PMID: 33732550      PMCID: PMC7950206          DOI: 10.7717/peerj.10975

Source DB:  PubMed          Journal:  PeerJ        ISSN: 2167-8359            Impact factor:   2.984


  46 in total

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Authors:  D Gordon E Robertson; James J Dowling
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2.  Elite sprinting: are athletes individually step-frequency or step-length reliant?

Authors:  Aki I T Salo; Ian N Bezodis; Alan M Batterham; David G Kerwin
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3.  Lower-limb mechanics during the support phase of maximum-velocity sprint running.

Authors:  Ian N Bezodis; David G Kerwin; Aki I T Salo
Journal:  Med Sci Sports Exerc       Date:  2008-04       Impact factor: 5.411

4.  OpenSim: open-source software to create and analyze dynamic simulations of movement.

Authors:  Scott L Delp; Frank C Anderson; Allison S Arnold; Peter Loan; Ayman Habib; Chand T John; Eran Guendelman; Darryl G Thelen
Journal:  IEEE Trans Biomed Eng       Date:  2007-11       Impact factor: 4.538

5.  How reliable are lower limb biomechanical variables during running and cutting tasks.

Authors:  Faisal Alenezi; Lee Herrington; Paul Jones; Richard Jones
Journal:  J Electromyogr Kinesiol       Date:  2016-07-01       Impact factor: 2.368

Review 6.  Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control.

Authors:  F E Zajac
Journal:  Crit Rev Biomed Eng       Date:  1989

7.  Kinetic demands of sprinting shift across the acceleration phase: Novel analysis of entire force waveforms.

Authors:  S L Colyer; R Nagahara; A I T Salo
Journal:  Scand J Med Sci Sports       Date:  2018-04-25       Impact factor: 4.221

8.  ISB recommendations on the reporting of intersegmental forces and moments during human motion analysis.

Authors:  Timothy R Derrick; Antonie J van den Bogert; Andrea Cereatti; Raphael Dumas; Silvia Fantozzi; Alberto Leardini
Journal:  J Biomech       Date:  2019-11-20       Impact factor: 2.712

9.  The force-velocity relationship of human adductor pollicis muscle during stretch and the effects of fatigue.

Authors:  C J Ruiter; W J Didden; D A Jones; A D Haan
Journal:  J Physiol       Date:  2000-08-01       Impact factor: 5.182

10.  Muscular strategy shift in human running: dependence of running speed on hip and ankle muscle performance.

Authors:  Tim W Dorn; Anthony G Schache; Marcus G Pandy
Journal:  J Exp Biol       Date:  2012-06-01       Impact factor: 3.312

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  1 in total

1.  Modifications to the net knee moments lead to the greatest improvements in accelerative sprinting performance: a predictive simulation study.

Authors:  Nicos Haralabidis; Steffi L Colyer; Gil Serrancolí; Aki I T Salo; Dario Cazzola
Journal:  Sci Rep       Date:  2022-09-23       Impact factor: 4.996

  1 in total

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