Climbing with Virtual Mentor by Means of Video-Based Motion Analysis

Julia Richter, Raul Beltrán, Guido Köstermeyer, Ulrich Heinkel

2023

Abstract

Due to the growing popularity of climbing, research on non-invasive, camera-based motion analysis has received increasing attention. While extant work uses invasive technologies, such as wearables or modified walls and holds, or focusses on competitive sports, we for the first time propose a system that automatically detects motion errors that are typical for beginners with a low level of climbing experience by means of video analysis. In our work, we imitate a virtual mentor that provides an analysis directly after having climbed a route. We thereby employed an iPad Pro fourth generation with LiDAR to record climbing sequences, in which the climber’s skeleton is extracted using the Vision framework provided by Apple. We adapted an existing method to detect joints movements and introduced a finite state machine that represents the repetitive phases that occur in climbing. By means of the detected movements, the current phase can be determined. Based on the phase, single errors that are only relevant in specific phases are extracted from the video sequence and presented to the climber. Latest empirical tests with 14 probands demonstrated the working principle. We are currently collecting data of climbing beginners for a quantitative evaluation of the proposed system.

Paper Citation

in Harvard Style

Richter J., Beltrán R., Köstermeyer G. and Heinkel U. (2023). Climbing with Virtual Mentor by Means of Video-Based Motion Analysis. In Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-642-2, SciTePress, pages 126-133. DOI: 10.5220/0011959300003497

in Bibtex Style

@conference{improve23,
author={Julia Richter and Raul Beltrán and Guido Köstermeyer and Ulrich Heinkel},
title={Climbing with Virtual Mentor by Means of Video-Based Motion Analysis},
booktitle={Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2023},
pages={126-133},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011959300003497},
isbn={978-989-758-642-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Climbing with Virtual Mentor by Means of Video-Based Motion Analysis
SN - 978-989-758-642-2
AU - Richter J.
AU - Beltrán R.
AU - Köstermeyer G.
AU - Heinkel U.
PY - 2023
SP - 126
EP - 133
DO - 10.5220/0011959300003497
PB - SciTePress