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

John Komar; Romain Hérault; Ludovic Seifert

Conference papers

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

John Komar ^{1, 2, 3} Romain Hérault ^{4, 5, 2, 6} Ludovic Seifert ^{1, 2, 3}

1 CETAPS - Centre d’études des transformations des activités physiques et sportives

2 NU - Normandie Université

3 UNIROUEN - Université de Rouen Normandie

4 DocApp - LITIS - Equipe Apprentissage

LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

5 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

6 INSA Rouen Normandie - Institut national des sciences appliquées Rouen Normandie

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.

Document type :

Conference papers

Domain :

Statistics [stat] / Machine Learning [stat.ML]

Complete list of metadatas

https://hal-normandie-univ.archives-ouvertes.fr/hal-02351767
Contributor : Romain Hérault <>
Submitted on : Wednesday, November 6, 2019 - 3:16:28 PM
Last modification on : Thursday, November 7, 2019 - 1:32:43 AM

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

Links full text

Identifiers

Collections

Citation

Export

Metrics

16

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

Links full text

Identifiers

Collections

Citation

Export

Share

Metrics

16