Addressing grading bias in rock climbing: machine and deep learning approaches
The determination rock climbing route difficulty is notoriously subjective. While there is no official standard for determining the difficulty of a rock climbing route, various difficulty rating scales exist. But as the sport gains more popularity and prominence on the international stage at the Oly...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Sports and Active Living |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspor.2024.1512010/full |
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| author | B. O’Mara M. S. Mahmud |
| author_facet | B. O’Mara M. S. Mahmud |
| author_sort | B. O’Mara |
| collection | DOAJ |
| description | The determination rock climbing route difficulty is notoriously subjective. While there is no official standard for determining the difficulty of a rock climbing route, various difficulty rating scales exist. But as the sport gains more popularity and prominence on the international stage at the Olympic Games, the need for standardized determination of route difficulty becomes more important. In commercial climbing gyms, consistency and accuracy in route production are crucial for success. Route setters often rely on personal judgment when determining route difficulty, but the success of commercial climbing gyms requires their objectivity in creating diverse, inclusive, and accurate routes. Machine and deep learning techniques have the potential to introduce a standardized form of route difficulty determination. This survey review categorizes machine and deep learning approaches taken, identifies the methods and algorithms used, reports their degree of success, and proposes areas of future work for determining route difficulty. The primary three approaches were from a route-centric, climber-centric, or path finding and path generation context. Of these, the most optimal methods used natural language processing or recurrent neural network algorithms. From these methods, it is argued that the objective difficulty of a rock climbing route has been best determined by route-centric, natural-language-like approaches. |
| format | Article |
| id | doaj-art-e70b3a674d34422d9a047fb5bf52ada4 |
| institution | DOAJ |
| issn | 2624-9367 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sports and Active Living |
| spelling | doaj-art-e70b3a674d34422d9a047fb5bf52ada42025-08-20T02:43:39ZengFrontiers Media S.A.Frontiers in Sports and Active Living2624-93672025-01-01610.3389/fspor.2024.15120101512010Addressing grading bias in rock climbing: machine and deep learning approachesB. O’MaraM. S. MahmudThe determination rock climbing route difficulty is notoriously subjective. While there is no official standard for determining the difficulty of a rock climbing route, various difficulty rating scales exist. But as the sport gains more popularity and prominence on the international stage at the Olympic Games, the need for standardized determination of route difficulty becomes more important. In commercial climbing gyms, consistency and accuracy in route production are crucial for success. Route setters often rely on personal judgment when determining route difficulty, but the success of commercial climbing gyms requires their objectivity in creating diverse, inclusive, and accurate routes. Machine and deep learning techniques have the potential to introduce a standardized form of route difficulty determination. This survey review categorizes machine and deep learning approaches taken, identifies the methods and algorithms used, reports their degree of success, and proposes areas of future work for determining route difficulty. The primary three approaches were from a route-centric, climber-centric, or path finding and path generation context. Of these, the most optimal methods used natural language processing or recurrent neural network algorithms. From these methods, it is argued that the objective difficulty of a rock climbing route has been best determined by route-centric, natural-language-like approaches.https://www.frontiersin.org/articles/10.3389/fspor.2024.1512010/fullrock climbingboulderingroute grade difficultydeep learningmachine learning |
| spellingShingle | B. O’Mara M. S. Mahmud Addressing grading bias in rock climbing: machine and deep learning approaches Frontiers in Sports and Active Living rock climbing bouldering route grade difficulty deep learning machine learning |
| title | Addressing grading bias in rock climbing: machine and deep learning approaches |
| title_full | Addressing grading bias in rock climbing: machine and deep learning approaches |
| title_fullStr | Addressing grading bias in rock climbing: machine and deep learning approaches |
| title_full_unstemmed | Addressing grading bias in rock climbing: machine and deep learning approaches |
| title_short | Addressing grading bias in rock climbing: machine and deep learning approaches |
| title_sort | addressing grading bias in rock climbing machine and deep learning approaches |
| topic | rock climbing bouldering route grade difficulty deep learning machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fspor.2024.1512010/full |
| work_keys_str_mv | AT bomara addressinggradingbiasinrockclimbingmachineanddeeplearningapproaches AT msmahmud addressinggradingbiasinrockclimbingmachineanddeeplearningapproaches |