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Cited by 742Lower Extremity Biomechanics and PathologiesMuscle activation and electromyography studiesStatistical Methods and Applications

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Vector field statistical analysis of kinematic and force trajectories

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Pataky, Todd C., Robinson, Mark A., Vanrenterghem, Jos·2013·Journal of Biomechanics
DOI 10.1016/j.jbiomech.2013.07.031
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Abstract

When investigating the dynamics of three-dimensional multi-body biomechanical systems it is often difficult to derive spatiotemporally directed predictions regarding experimentally induced effects. A paradigm of 'non-directed' hypothesis testing has emerged in the literature as a result. Non-directed analyses typically consist of ad hoc scalar extraction, an approach which substantially simplifies the original, highly multivariate datasets (many time points, many vector components). This paper describes a commensurately multivariate method as an alternative to scalar extraction. The method, called 'statistical parametric mapping' (SPM), uses random field theory to objectively identify field regions which co-vary significantly with the experimental design. We compared SPM to scalar extraction by re-analyzing three publicly available datasets: 3D knee kinematics, a ten-muscle force system, and 3D ground reaction forces. Scalar extraction was found to bias the analyses of all three datasets by failing to consider sufficient portions of the dataset, and/or by failing to consider covariance amongst vector components. SPM overcame both problems by conducting hypothesis testing at the (massively multivariate) vector trajectory level, with random field corrections simultaneously accounting for temporal correlation and vector covariance. While SPM has been widely demonstrated to be effective for analyzing 3D scalar fields, the current results are the first to demonstrate its effectiveness for 1D vector field analysis. It was concluded that SPM offers a generalized, statistically comprehensive solution to scalar extraction's over-simplification of vector trajectories, thereby making it useful for objectively guiding analyses of complex biomechanical systems.

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Metadata

Authors
Pataky, T. C., Robinson, M. A., & Vanrenterghem, J.
Affiliations
  • Shinshu University (JP)
  • Liverpool John Moores University (GB)
Corresponding author
Pataky, Todd C.
ORCID
Venue
Journal of Biomechanics
Publisher
Elsevier BV
Published
2013-09
Volume
46
Issue
14
Pages
2394-2401
ISSN
0021-9290 (ISSN-L)
DOI
10.1016/j.jbiomech.2013.07.031
OpenAlex
W2080500625
Cited by
741
References
25
Citations by year
2026: 56 · 2025: 102 · 2024: 90 · 2023: 72 · 2022: 82 · 2021: 90 · 2020: 63 · 2019: 60 · 2018: 47 · 2017: 34 · 2016: 31 · 2015: 8 · 2014: 5 · 2013: 1
FWCI
8.28
Type
journal article
Language
en
Update notices
erratum (2015-01-02)
Open access
Licence
Locations
  • Journal of Biomechanics (publishedVersion)
  • PubMed (publishedVersion)
  • SOAR (Shinshu University) (submittedVersion, cc-by-nc-nd)
  • Institutional Repositories DataBase (IRDB) (submittedVersion)
Funders
JSPS Wakate B (22700465)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
statistical parametric mapping, random field theory, vector field analysis, kinematic trajectories, ground reaction force, knee kinematics, temporal correlation, hypothesis testing, biomechanical systems
MeSH terms
Biomechanical Phenomena, Data Interpretation, Statistical, Humans, Knee, Rotation, Running, Models, Statistical, Walking, Muscle, Skeletal, Musculoskeletal Pain

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