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Towards Automatic Object Detection and Activity Recognition in Indoor Climbing.

Hana Vrzáková1, Jani Koskinen1, Sami Andberg1

  • 1School of Computing, University of Eastern Finland, FI-80101 Joensuu, Finland.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary
This summary is machine-generated.

Expert rock climbers

Keywords:
boulderingdeep learningexpertiseeye trackingperceptual-motor control

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Area of Science:

  • Sport Science
  • Cognitive Neuroscience
  • Human-Computer Interaction

Background:

  • Rock climbing is a high-stakes perception-action task requiring integrated cognition, perception, and movement.
  • Previous research primarily focused on climbers' movement, with limited understanding of visual attention's role.
  • Manual coding of eye-tracking data for analyzing climber attention is time-consuming and labor-intensive.

Purpose of the Study:

  • To develop a deep learning framework for automatic grasp recognition in indoor climbing.
  • To automatically contextualize eye movement analysis with climbing actions.
  • To investigate the relationship between expert climbers' visual attention and grasping behavior.

Main Methods:

  • Utilized deep learning (YOLOv5) for automatic hold detection and grasp recognition.
  • Employed eye-tracking glasses (SMI and Tobii Glasses 2) to record expert climbers' eye movements and egocentric perspective.
  • Correlated grasping duration with fixation duration, fixation count, fixation rate, and saccade rate.

Main Results:

  • Expert climber grasping duration positively correlated with total fixation duration (r=0.807) and fixation count (r=0.864).
  • Grasping duration negatively correlated with fixation rate (r=-0.402) and saccade rate (r=-0.344).
  • Findings suggest cognitive processing and visual search during decision-making and route prospecting.

Conclusions:

  • The developed framework enables automatic analysis of eye movements in climbing.
  • Results provide insights into expert climbers' visual attention strategies and decision-making processes.
  • This research contributes to understanding eye-body coordination in high-stakes activities and informs training optimization and coaching.