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Fatigue-Related and Timescale-Dependent Changes in Individual Movement Patterns Identified Using Support Vector Machine

Authors
  • Burdack, Johannes1
  • Horst, Fabian1
  • Aragonés, Daniel1
  • Eekhoff, Alexander1
  • Schöllhorn, Wolfgang Immanuel1, 2
  • 1 Department of Training and Movement Science, Institute of Sports Science, Johannes Gutenberg University Mainz, Mainz , (Germany)
  • 2 Department of Wushu, School of Martial Arts, Shanghai University of Sport, Shanghai , (China)
Type
Published Article
Journal
Frontiers in Psychology
Publisher
Frontiers Media SA
Publication Date
Sep 30, 2020
Volume
11
Identifiers
DOI: 10.3389/fpsyg.2020.551548
PMID: 33101124
PMCID: PMC7554555
Source
PubMed Central
Keywords
License
Unknown

Abstract

The scientific and practical fields—especially high-performance sports—increasingly request a stronger focus be placed on individual athletes in human movement science research. Machine learning methods have shown efficacy in this context by identifying the unique movement patterns of individuals and distinguishing their intra-individual changes over time. The objective of this investigation is to analyze biomechanically described movement patterns during the fatigue-related accumulation process within a single training session of a high number of repeated executions of a ballistic sports movement—specifically, the frontal foot kick ( mae-geri ) in karate—in expert athletes. The two leading research questions presented for consideration are (1) Can characteristics of individual movement patterns be observed throughout the entire training session despite continuous changes, i.e., even as fatigue-related processes increase? and (2) How do intra-individual movement patterns change as fatigue-related processes increase throughout a training session? Sixteen expert karatekas performed 606 frontal foot kicks directed toward an imaginary target. The kicks were performed in nine sets at 80% ( K -80) of the self-experienced maximal intensity. In addition, six kicks at maximal intensity ( K -100) were performed after each of the nine sets. Between the sets, the participants took a 90-s break. Three-dimensional full-body kinematic data of all kicks were recorded with 10 infrared cameras. The normalized waveforms of nine upper- and lower-body joint angles were classified using a supervised machine learning method (support vector machine). The results of the classification revealed a disjunct distinction between the kinematic movement patterns of individual athletes. The identification of unique movement patterns of individual athletes was independent of the intensity and the degree of fatigue-related processes. In other words, even with the accumulation of fatigue-related processes, the unique movement patterns of an individual athlete can be clearly identified. During the training session, changes in intra-individual movement patterns could also be detected, indicating the occurrence of adaptations in individual movement patterns throughout the fatigue-related accumulation process. The results suggest that these adaptations can be modeled in terms of changes in patterns rather than increasing variance. Practical consequences are critically discussed.

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