Authors:
Julia Richter
1
;
Raul Beltrán
1
;
Guido Köstermeyer
2
and
Ulrich Heinkel
1
Affiliations:
1
Professorship Circuit and System Design, Chemnitz University of Technology, Reichenhainer Straße 70, Chemnitz, Germany
;
2
Department Sportwissenschaft und Sport, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schlossplatz 4, 91054 Erlangen, Germany
Keyword(s):
Computer Vision, Human Pose Estimation, Climbing Motion Analysis, Feedback Systems.
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 a
re 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.
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