Addressing grading bias in rock climbing: machine and deep learning approaches

Area of Science:

• Sports Science
• Computer Science
• Artificial Intelligence

Background:

• Rock climbing route difficulty assessment is subjective, lacking standardized methods.
• The growing popularity of climbing, including its Olympic inclusion, highlights the need for objective difficulty determination.
• Commercial climbing gyms require accurate and consistent route grading for success and inclusivity.

Purpose of the Study:

• To survey and categorize machine and deep learning approaches for determining rock climbing route difficulty.
• To identify effective methods and algorithms for objective route grading.
• To propose future research directions in standardized difficulty assessment.

Main Methods:

• Categorization of machine and deep learning approaches into route-centric, climber-centric, and path-finding contexts.
• Identification and analysis of algorithms, including natural language processing (NLP) and recurrent neural networks (RNNs).
• Review of reported success rates for different methodologies.

Main Results:

• Route-centric approaches, especially those utilizing natural language processing, demonstrated the most optimal results.
• Recurrent neural networks were also identified as effective algorithms in this domain.
• The study identified NLP-based, route-centric methods as the leading approach for objective difficulty determination.

Conclusions:

• Machine and deep learning techniques hold significant potential for standardizing rock climbing route difficulty.
• NLP-based, route-centric methods represent the most effective current approach for objective difficulty assessment.
• Further research is needed to refine these models and implement them widely.