Full-body movement pattern recognition in climbing*

Ludovic Seifert*, Vladislavs Dovgalecs, Jérémie Boulanger, Dominic Orth, Romain Hérault, Keith Davids

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The aim of this study was to propose a method for full-body movement pattern recognition in climbing, by computing the 3D unitary vector of the four limbs and pelvis during performance. One climber with an intermediate skill level traversed two easy routes of similar rates of difficulty (5c difficulty on French scale), 10m in height under top-rope conditions. The first route was simply designed to allow horizontal edge-hold grasping, while the second route was designed with more complexity to allow both horizontal and vertical edge-hold grasping. Five inertial measurement units (IMUs) were attached to the pelvis, both feet and forearms to analyse the 3D unitary vector of each limb and pelvis. Cluster analysis was performed to detect the number of clusters that emerged from coordination of the four limbs and pelvis during climbing performance. Analysis revealed 22 clusters with 11 clusters unique across the two routes. Six clusters were unique to the simple hold design route and five clusters emerged only in the complex hold design route. We conclude that clustering supported identification of full-body orientations during traversal, representing a level of analysis that can provide useful information for performance monitoring in climbing.

Original languageEnglish
Pages (from-to)166-173
Number of pages8
JournalSports Technology
Volume7
Issue number3-4
DOIs
Publication statusPublished - 2 Oct 2014
Externally publishedYes

Keywords

  • climbing skills
  • cluster analysis
  • inertial measurement unit
  • motor control
  • pattern recognition

Fingerprint

Dive into the research topics of 'Full-body movement pattern recognition in climbing<sup>*</sup>'. Together they form a unique fingerprint.

Cite this