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Review of Modelling Techniques for In Vivo Muscle Force Estimation in the Lower Extremities during Strength Training
Background. Knowledge of the musculoskeletal loading conditions during strength training is essential for performance monitoring, injury prevention, rehabilitation, and training design. However, measuring muscle forces during exercise performance as a primary determinant of training efficacy and saf...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4568356/ https://www.ncbi.nlm.nih.gov/pubmed/26417378 http://dx.doi.org/10.1155/2015/483921 |
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author | Schellenberg, Florian Oberhofer, Katja Taylor, William R. Lorenzetti, Silvio |
author_facet | Schellenberg, Florian Oberhofer, Katja Taylor, William R. Lorenzetti, Silvio |
author_sort | Schellenberg, Florian |
collection | PubMed |
description | Background. Knowledge of the musculoskeletal loading conditions during strength training is essential for performance monitoring, injury prevention, rehabilitation, and training design. However, measuring muscle forces during exercise performance as a primary determinant of training efficacy and safety has remained challenging. Methods. In this paper we review existing computational techniques to determine muscle forces in the lower limbs during strength exercises in vivo and discuss their potential for uptake into sports training and rehabilitation. Results. Muscle forces during exercise performance have almost exclusively been analysed using so-called forward dynamics simulations, inverse dynamics techniques, or alternative methods. Musculoskeletal models based on forward dynamics analyses have led to considerable new insights into muscular coordination, strength, and power during dynamic ballistic movement activities, resulting in, for example, improved techniques for optimal performance of the squat jump, while quasi-static inverse dynamics optimisation and EMG-driven modelling have helped to provide an understanding of low-speed exercises. Conclusion. The present review introduces the different computational techniques and outlines their advantages and disadvantages for the informed usage by nonexperts. With sufficient validation and widespread application, muscle force calculations during strength exercises in vivo are expected to provide biomechanically based evidence for clinicians and therapists to evaluate and improve training guidelines. |
format | Online Article Text |
id | pubmed-4568356 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-45683562015-09-28 Review of Modelling Techniques for In Vivo Muscle Force Estimation in the Lower Extremities during Strength Training Schellenberg, Florian Oberhofer, Katja Taylor, William R. Lorenzetti, Silvio Comput Math Methods Med Review Article Background. Knowledge of the musculoskeletal loading conditions during strength training is essential for performance monitoring, injury prevention, rehabilitation, and training design. However, measuring muscle forces during exercise performance as a primary determinant of training efficacy and safety has remained challenging. Methods. In this paper we review existing computational techniques to determine muscle forces in the lower limbs during strength exercises in vivo and discuss their potential for uptake into sports training and rehabilitation. Results. Muscle forces during exercise performance have almost exclusively been analysed using so-called forward dynamics simulations, inverse dynamics techniques, or alternative methods. Musculoskeletal models based on forward dynamics analyses have led to considerable new insights into muscular coordination, strength, and power during dynamic ballistic movement activities, resulting in, for example, improved techniques for optimal performance of the squat jump, while quasi-static inverse dynamics optimisation and EMG-driven modelling have helped to provide an understanding of low-speed exercises. Conclusion. The present review introduces the different computational techniques and outlines their advantages and disadvantages for the informed usage by nonexperts. With sufficient validation and widespread application, muscle force calculations during strength exercises in vivo are expected to provide biomechanically based evidence for clinicians and therapists to evaluate and improve training guidelines. Hindawi Publishing Corporation 2015 2015-08-31 /pmc/articles/PMC4568356/ /pubmed/26417378 http://dx.doi.org/10.1155/2015/483921 Text en Copyright © 2015 Florian Schellenberg et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Schellenberg, Florian Oberhofer, Katja Taylor, William R. Lorenzetti, Silvio Review of Modelling Techniques for In Vivo Muscle Force Estimation in the Lower Extremities during Strength Training |
title | Review of Modelling Techniques for In Vivo Muscle Force Estimation in the Lower Extremities during Strength Training |
title_full | Review of Modelling Techniques for In Vivo Muscle Force Estimation in the Lower Extremities during Strength Training |
title_fullStr | Review of Modelling Techniques for In Vivo Muscle Force Estimation in the Lower Extremities during Strength Training |
title_full_unstemmed | Review of Modelling Techniques for In Vivo Muscle Force Estimation in the Lower Extremities during Strength Training |
title_short | Review of Modelling Techniques for In Vivo Muscle Force Estimation in the Lower Extremities during Strength Training |
title_sort | review of modelling techniques for in vivo muscle force estimation in the lower extremities during strength training |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4568356/ https://www.ncbi.nlm.nih.gov/pubmed/26417378 http://dx.doi.org/10.1155/2015/483921 |
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