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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.00458 |
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| _version_ | 1866913716041154560 |
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| author | Rimbot, Thomas Jaggi, Martin Barba, Luis |
| author_facet | Rimbot, Thomas Jaggi, Martin Barba, Luis |
| contents | In this work, we investigate the application of Machine Learning techniques to sport climbing. Expanding upon previous projects, we develop a visualization tool for move sequence evaluation on a given boulder. Then, we look into move sequence prediction from simple holds sequence information using three different Transformer models. While the results are not conclusive, they are a first step in this kind of approach and lay the ground for future work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using Machine Learning for move sequence visualization and generation in climbing Rimbot, Thomas Jaggi, Martin Barba, Luis Machine Learning Computer Vision and Pattern Recognition In this work, we investigate the application of Machine Learning techniques to sport climbing. Expanding upon previous projects, we develop a visualization tool for move sequence evaluation on a given boulder. Then, we look into move sequence prediction from simple holds sequence information using three different Transformer models. While the results are not conclusive, they are a first step in this kind of approach and lay the ground for future work. |
| title | Using Machine Learning for move sequence visualization and generation in climbing |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.00458 |