Kinematic gait patterns in healthy runners: A hierarchical cluster analysis

Phinyomark, Angkoon; Osis, Sean T.; Hettinga, Blayne A.; Ferber, Reed

doi:10.1016/j.jbiomech.2015.09.025

Cited by 76 publications

(52 citation statements)

References 26 publications

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“…The final number of subgroups was chosen based on a stopping rule (a large percentage decrease in the coefficient followed by a plateau) . The number of subgroups was also confirmed by visual inspection of the dendrogram …”

Section: Methodsmentioning

confidence: 99%

“…After forming the subgroups, a univariate analysis of variance (ANOVA) was used to determine which PCs separated each subgroup from the others. 17 The PCs were then interpreted by calculating the loadings of variables to determine which variables comprised the PCs. 34 Demographic information (height, weight, age, and running speed) of the subgroups was also compared using ANOVA.…”

Section: Interpretation and Comparison Of Subgroupsmentioning

confidence: 99%

“…Unsupervised machine learning works by discovering underlying patterns and associations in datasets without any a priori information aside from a set of input variables . These statistical methods have demonstrated success in uncovering underlying structure within datasets in previous sport science research . For example, race patterns in elite swimmers, different golf swing patterns, and different gait patterns in injured and healthy runners have all been successfully identified using unsupervised machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

“…12 These statistical methods have demonstrated success in uncovering underlying structure within datasets in previous sport science research. [13][14][15][16][17] For example, race patterns in elite swimmers, 13 different golf swing patterns, 14 and different gait patterns in injured and healthy runners [15][16][17] have all been successfully identified using unsupervised machine learning methods. Another study used unsupervised machine learning to create a movement profile for healthy controls walking and assessed the deviation of patients with gait problems from this profile.…”

Section: Introductionmentioning

confidence: 99%

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A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Jauhiainen

Pohl

Äyrämö

et al. 2020

Scandinavian Med Sci Sports

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Previous studies have suggested that runners can be subgrouped based on homogeneous gait patterns; however, no previous study has assessed the presence of such subgroups in a population of individuals across a wide variety of injuries. Therefore, the purpose of this study was to assess whether distinct subgroups with homogeneous running patterns can be identified among a large group of injured and healthy runners and whether identified subgroups are associated with specific injury location. Three‐dimensional kinematic data from 291 injured and healthy runners, representing both sexes and a wide range of ages (10‐66 years), were clustered using hierarchical cluster analysis. Cluster analysis revealed five distinct subgroups from the data. Kinematic differences between the subgroups were compared using one‐way analysis of variance (ANOVA). Against our hypothesis, runners with the same injury types did not cluster together, but the distribution of different injuries within subgroups was similar across the entire sample. These results suggest that homogeneous gait patterns exist independent of injury location and that it is important to consider these underlying patterns when planning injury prevention or rehabilitation strategies.

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Section: Methodsmentioning

confidence: 99%

Section: Interpretation and Comparison Of Subgroupsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Jauhiainen

Pohl

Äyrämö

et al. 2020

Scandinavian Med Sci Sports

Self Cite

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show abstract

“…Many biomechanical investigations examine groups based on injury criteria (e.g., patellofemoral pain), demographic factors (e.g., age, height, sex), or gait speed, under the assumption that movement patterns will be homogeneous within these groups. By contrast, we used a data science approach to identify two distinct kinematic running gait patterns within a single group of healthy runners (Phinyomark et al, 2015). The dataset was collected over a period of two years as part of an initiative to build a database of running and walking biomechanics.…”

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confidence: 99%

Gait biomechanics in the era of data science

Ferber

Osis

Hicks

et al. 2016

Journal of Biomechanics

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Data science has transformed fields such as computer vision and economics. The ability of modern data science methods to extract insights from large, complex, heterogeneous, and noisy datasets is beginning to provide a powerful complement to the traditional approaches of experimental motion capture and biomechanical modeling. The purpose of this article is to provide a perspective on how data science methods can be incorporated into our field to advance our understanding of gait biomechanics and improve treatment planning procedures. We provide examples of how data science approaches have been applied to biomechanical data. We then discuss the challenges that remain for effectively using data science approaches in clinical gait analysis and gait biomechanics research, including the need for new tools, better infrastructure and incentives for sharing data, and education across the disciplines of biomechanics and data science. By addressing these challenges, we can revolutionize treatment planning and biomechanics research by capitalizing on the wealth of knowledge gained by gait researchers over the past decades and the vast, but often siloed, data that are collected in clinical and research laboratories around the world.

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The development of mature gait patterns in children during walking and running

2021

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Purpose We sought to identify the developing maturity of walking and running in young children. We assessed gait patterns for the presence of flight and double support phases complemented by mechanical energetics. The corresponding classification outcomes were contrasted via a shotgun approach involving several potentially informative gait characteristics. A subsequent clustering turned out very effective to classify the degree of gait maturity. Methods Participants (22 typically developing children aged 2–9 years and 7 young, healthy adults) walked/ran on a treadmill at comfortable speeds. We determined double support and flight phases and the relationship between potential and kinetic energy oscillations of the center-of-mass. Based on the literature, we further incorporated a total of 93 gait characteristics (including the above-mentioned ones) and employed multivariate statistics comprising principal component analysis for data compression and hierarchical clustering for classification. Results While the ability to run including a flight phase increased with age, the flight phase did not reach 20% of the gait cycle. It seems that children use a walk-run-strategy when learning to run. Yet, the correlation strength between potential and kinetic energies saturated and so did the amount of recovered mechanical energy. Clustering the set of gait characteristics allowed for classifying gait in more detail. This defines a metric for maturity in terms of deviations from adult gait, which disagrees with chronological age. Conclusions The degree of gait maturity estimated statistically using various gait characteristics does not always relate directly to the chronological age of the child.

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Kinematic gait patterns in healthy runners: A hierarchical cluster analysis

Cited by 76 publications

References 26 publications

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Gait biomechanics in the era of data science

The development of mature gait patterns in children during walking and running

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