Version 1
: Received: 8 February 2023 / Approved: 9 February 2023 / Online: 9 February 2023 (11:23:49 CET)
How to cite:
Pieprzycki, A.; Mazur, T.; Krawczyk, M.; Król, D.; Witek, M.; Rokowski, R. Computer-Aided Methods for Analysing Run of Speed Climbers. Preprints2023, 2023020166. https://doi.org/10.20944/preprints202302.0166.v1
Pieprzycki, A.; Mazur, T.; Krawczyk, M.; Król, D.; Witek, M.; Rokowski, R. Computer-Aided Methods for Analysing Run of Speed Climbers. Preprints 2023, 2023020166. https://doi.org/10.20944/preprints202302.0166.v1
Pieprzycki, A.; Mazur, T.; Krawczyk, M.; Król, D.; Witek, M.; Rokowski, R. Computer-Aided Methods for Analysing Run of Speed Climbers. Preprints2023, 2023020166. https://doi.org/10.20944/preprints202302.0166.v1
APA Style
Pieprzycki, A., Mazur, T., Krawczyk, M., Król, D., Witek, M., & Rokowski, R. (2023). Computer-Aided Methods for Analysing Run of Speed Climbers. Preprints. https://doi.org/10.20944/preprints202302.0166.v1
Chicago/Turabian Style
Pieprzycki, A., Maciej Witek and Robert Rokowski. 2023 "Computer-Aided Methods for Analysing Run of Speed Climbers" Preprints. https://doi.org/10.20944/preprints202302.0166.v1
Abstract
The continuously developing project aims to build an informatics system enabling analysis of spatial and temporal parameters of movement activities occurring in the sport of speed climbing. The monitoring system (climbing information speed system – CISS) is to be used for conducting comprehensive scientific research in the field of speed climbing. The system enables the evaluation of the training process of climbers at various levels of competition. The study analysis was based on video. The video recording with a camera positioned at a short distance (10 m) from the wall. The marker was positioned closest to the centre of mass (gravity) BMC. Results: development of a system for data collection and analysis of the climbing run based on video recording (application of the Kanade-Lucas-Tomasi (KLT) algorithm). Our results showed that used devices can measure a wide range of specific internal and external variables during speed climbing. Some of the analyzed parameters were significantly correlated with speed climbing time. These results could be a theoretical basis for future research and for training program’s preparation.
Keywords
speed sport climbing; video analysis; KLT algorithm; Convolutional Neural Network (CNN); OpenPose; AI; artificial intelligence
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
