Key point selection and clustering of swimmer coordination through Sparse Fisher-EM

2014-01-07 · John Komar, Romain Hérault, Ludovic Seifert

To answer the existence of optimal swimmer learning/teaching strategies, this work introduces a two-level clustering in order to analyze temporal dynamics of motor learning in breaststroke swimming. Each level have been performed through Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on large and correlated datasets. The induced sparsity selects key points of the coordination phase without any prior knowledge.

📄 PDF Abstract BibTeX arXiv:1401.1489

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