IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0171535.html
   My bibliography  Save this article

Comparison of muscle synergies for running between different foot strike patterns

Author

Listed:
  • Koji Nishida
  • Shota Hagio
  • Benio Kibushi
  • Toshio Moritani
  • Motoki Kouzaki

Abstract

It is well known that humans run with a fore-foot strike (FFS), a mid-foot strike (MFS) or a rear-foot strike (RFS). A modular neural control mechanism of human walking and running has been discussed in terms of muscle synergies. However, the neural control mechanisms for different foot strike patterns during running have been overlooked even though kinetic and kinematic differences between different foot strike patterns have been reported. Thus, we examined the differences in the neural control mechanisms of human running between FFS and RFS by comparing the muscle synergies extracted from each foot strike pattern during running. Muscle synergies were extracted using non-negative matrix factorization with electromyogram activity recorded bilaterally from 12 limb and trunk muscles in ten male subjects during FFS and RFS running at different speeds (5–15 km/h). Six muscle synergies were extracted from all conditions, and each synergy had a specific function and a single main peak of activity in a cycle. The six muscle synergies were similar between FFS and RFS as well as across subjects and speeds. However, some muscle weightings showed significant differences between FFS and RFS, especially the weightings of the tibialis anterior of the landing leg in synergies activated just before touchdown. The activation patterns of the synergies were also different for each foot strike pattern in terms of the timing, duration, and magnitude of the main peak of activity. These results suggest that the central nervous system controls running by sending a sequence of signals to six muscle synergies. Furthermore, a change in the foot strike pattern is accomplished by modulating the timing, duration and magnitude of the muscle synergy activity and by selectively activating other muscle synergies or subsets of the muscle synergies.

Suggested Citation

  • Koji Nishida & Shota Hagio & Benio Kibushi & Toshio Moritani & Motoki Kouzaki, 2017. "Comparison of muscle synergies for running between different foot strike patterns," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-19, February.
  • Handle: RePEc:plo:pone00:0171535
    DOI: 10.1371/journal.pone.0171535
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171535
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0171535&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0171535?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Daniel E. Lieberman & Madhusudhan Venkadesan & William A. Werbel & Adam I. Daoud & Susan D’Andrea & Irene S. Davis & Robert Ojiambo Mang’Eni & Yannis Pitsiladis, 2010. "Foot strike patterns and collision forces in habitually barefoot versus shod runners," Nature, Nature, vol. 463(7280), pages 531-535, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rafael Teixeira & Mário Antunes & Diogo Gomes & Rui L. Aguiar, 2024. "Comparison of Semantic Similarity Models on Constrained Scenarios," Information Systems Frontiers, Springer, vol. 26(4), pages 1307-1330, August.
    2. Del Corso, Gianna M. & Romani, Francesco, 2019. "Adaptive nonnegative matrix factorization and measure comparisons for recommender systems," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 164-179.
    3. P Fogel & C Geissler & P Cotte & G Luta, 2022. "Applying separative non-negative matrix factorization to extra-financial data," Working Papers hal-03689774, HAL.
    4. Spelta, A. & Pecora, N. & Rovira Kaltwasser, P., 2019. "Identifying Systemically Important Banks: A temporal approach for macroprudential policies," Journal of Policy Modeling, Elsevier, vol. 41(1), pages 197-218.
    5. Paul Fogel & Yann Gaston-Mathé & Douglas Hawkins & Fajwel Fogel & George Luta & S. Stanley Young, 2016. "Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health," IJERPH, MDPI, vol. 13(5), pages 1-14, May.
    6. Le Thi Khanh Hien & Duy Nhat Phan & Nicolas Gillis, 2022. "Inertial alternating direction method of multipliers for non-convex non-smooth optimization," Computational Optimization and Applications, Springer, vol. 83(1), pages 247-285, September.
    7. Jingfeng Guo & Chao Zheng & Shanshan Li & Yutong Jia & Bin Liu, 2022. "BiInfGCN: Bilateral Information Augmentation of Graph Convolutional Networks for Recommendation," Mathematics, MDPI, vol. 10(17), pages 1-16, August.
    8. Jianfei Cao & Han Yang & Jianshu Lv & Quanyuan Wu & Baolei Zhang, 2023. "Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    9. Zhang, Lifeng & Chao, Xiangrui & Qian, Qian & Jing, Fuying, 2022. "Credit evaluation solutions for social groups with poor services in financial inclusion: A technical forecasting method," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    10. Yi Yu & Jaeseung Baek & Ali Tosyali & Myong K. Jeong, 2024. "Robust asymmetric non-negative matrix factorization for clustering nodes in directed networks," Annals of Operations Research, Springer, vol. 341(1), pages 245-265, October.
    11. Wentao Qu & Xianchao Xiu & Huangyue Chen & Lingchen Kong, 2023. "A Survey on High-Dimensional Subspace Clustering," Mathematics, MDPI, vol. 11(2), pages 1-39, January.
    12. Anna Luiza Silva Almeida Vicente & Alexei Novoloaca & Vincent Cahais & Zainab Awada & Cyrille Cuenin & Natália Spitz & André Lopes Carvalho & Adriane Feijó Evangelista & Camila Souza Crovador & Rui Ma, 2022. "Cutaneous and acral melanoma cross-OMICs reveals prognostic cancer drivers associated with pathobiology and ultraviolet exposure," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    13. Takehiro Sano & Tsuyoshi Migita & Norikazu Takahashi, 2022. "A novel update rule of HALS algorithm for nonnegative matrix factorization and Zangwill’s global convergence," Journal of Global Optimization, Springer, vol. 84(3), pages 755-781, November.
    14. Adam R. Pines & Bart Larsen & Zaixu Cui & Valerie J. Sydnor & Maxwell A. Bertolero & Azeez Adebimpe & Aaron F. Alexander-Bloch & Christos Davatzikos & Damien A. Fair & Ruben C. Gur & Raquel E. Gur & H, 2022. "Dissociable multi-scale patterns of development in personalized brain networks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    15. Xiangli Li & Hongwei Liu & Xiuyun Zheng, 2012. "Non-monotone projection gradient method for non-negative matrix factorization," Computational Optimization and Applications, Springer, vol. 51(3), pages 1163-1171, April.
    16. Ding, Chris & Li, Tao & Peng, Wei, 2008. "On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3913-3927, April.
    17. Dominik P. Koller & Michael Schirner & Petra Ritter, 2024. "Human connectome topology directs cortical traveling waves and shapes frequency gradients," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    18. Abdul Suleman, 2017. "On ill-conceived initialization in archetypal analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 785-808, December.
    19. Lu, Hong & Sang, Xiaoshuang & Zhao, Qinghua & Lu, Jianfeng, 2020. "Community detection algorithm based on nonnegative matrix factorization and pairwise constraints," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    20. Emelia Opoku Aboagye & Rajesh Kumar, 2019. "Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions," Future Internet, MDPI, vol. 11(1), pages 1-16, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0171535. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.