Key point selection and clustering of swimmer coordination through Sparse Fisher-EM
This work addresses motor learning analysis for swimmers, but it appears incremental as it applies an existing unsupervised framework to a specific domain.
The paper tackled the problem of identifying optimal swimmer learning/teaching strategies by analyzing temporal dynamics of motor learning in breaststroke swimming, using a two-level clustering approach with Sparse Fisher-EM to select key coordination points without prior knowledge.
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.