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

TL;DR

This paper introduces a two-level clustering method using Sparse Fisher-EM to analyze swimmer coordination dynamics, efficiently selecting key points without prior knowledge, aiding understanding of motor learning in breaststroke swimming.

Contribution

It presents a novel two-level clustering approach with Sparse Fisher-EM for analyzing swimming coordination, highlighting key points without prior assumptions.

Findings

Efficient identification of key coordination points in swimming data

Effective analysis of motor learning dynamics in breaststroke

Applicable to large, correlated datasets

Abstract

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.