Full-body movement pattern recognition in climbing^*

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.

Keywords::

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

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This project received the support of the CPER/GRR1880 Logistic, Mobility and Numeric and FEDER RISC [grant number 33172]. This project also received the funding of the French National Agency of Research [grant number ANR-13-JSH2-0004 DynaMov]. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.