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A time-series based pattern recognition method to analyze rollover kinematics in running

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Footwear science struggles at identifying effects of footwear interventions on the biomechanics of running with many studies affected or biased by small samples, small effect sizes and substantial interindividual variation,… Click to show full abstract

Footwear science struggles at identifying effects of footwear interventions on the biomechanics of running with many studies affected or biased by small samples, small effect sizes and substantial interindividual variation, among other factors. This has led to ongoing discussions about alternating paradigms concerning the effects of footwear on motor behaviour, performance and injury risk. However, as some authors have pointed out (Hamill, Boyer, & Weir, 2012; Vanwanseele, Zhang, & Sch€utte, 2018), methodological considerations rather than faulty paradigms may play a major role in the current lack of evidence that can be drawn from footwear research. Dimensionality reduction procedures applied to continuous waveforms of biomechanical data are intended to facilitate the applicability of traditional linear statistical models, but at the same time, discard much of the information content of the original data.

Keywords: pattern recognition; based pattern; time series; series based; time; kinematics

Journal Title: Footwear Science
Year Published: 2019

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