Self-Organizing Maps for the Analysis of Complex Movement Patterns
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We apply the Self-Organizing-Map-algorithm (SOM) as a central processing step in a new scheme for the characterisation of movement patterns of athletes. Due to its non-linear dimension reduction capabilities, the SOM outperforms a direct processing of the data as well as preprocessing using principal component analysis. Our results open the way to an objective assessment of movement patterns, with possible applications in the sport sciences as well as in medicine.
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