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Communication Dans Un Congrès Année : 2015

Valuation of climbing activities using Multi-Scale Jensen-Shannon Neighbour Embedding

Résumé

This paper presents a study carried out in a controlled environment that aims at understanding behavioural patterns in climbing activities. Multi-Scale Jensen-Shannon Neighbour Embedding [8], a recent advance in non linear dimension reduction, has been applied to recordings of movement sensors in order to help the visualization of coordination modes. Initial clustering results show a correlation with jerk, an indicator of fluency in climbing activities, but provides more details on behavioural patterns.
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Dates et versions

hal-01441636 , version 1 (20-01-2017)

Identifiants

  • HAL Id : hal-01441636 , version 1

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Romain Herault, Jeremie Boulanger, Ludovic Seifert, John Aldo Lee. Valuation of climbing activities using Multi-Scale Jensen-Shannon Neighbour Embedding. Machine Learning and Data Mining for Sports Analytics, ECML/PKDD 2015 workshop (MLSA2015), Sep 2015, Porto, Portugal. ⟨hal-01441636⟩
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