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The correct classification of runners according to their gait patterns is a relevant issue for the design of sports footwear. Specifically, the classification of runners as neutral, pronators, and supinators is a problem that is not yet fully solved, and requires expert observation, since current models based on the automatic processing of kinematic measures are very limited. This work proposes a method based on Functional Data Analysis (FDA) for automatically describing the morphology of the curves that represent ankle movement patterns. By Functional Analysis of Principal Components, the information contained in each data stream is reduced to a small set of variables, that allows an efficient classification of subjects.

Identification of Pronation-supination Patterns on Runners - An Aplication of Functional Principal Component Analysis