Masterarbeit
Al-Powered Bouldering Route Recommendation: Using Generative Al to
Identify Optimal Climbing Paths for Beginners
Completion
2025/01
Research Area
Students
Ketjona Lepuri
Advisers
Prof. Dr. Lewis Chuang
Dr.-Ing. Sebastian Heil
Description
This thesis explores the potential of Generative AI in recommending climbing routes for beginner bouldering climbers, our primary target group. Many beginners struggle to identify routes that match their ability levels, making the climbing experience challenging. This study aims to address this issue by developing an AI-powered Web Application that assists beginners in selecting suitable and safe routes, improving their climbing experience.
The research begins with a comprehensive review of AI models appropriate for route generation, followed by a market analysis to examine existing AI applications and identify their limitations. Additionally, expert interviews will be conducted to identify and refine the key parameters needed for accurate route recommendations. These insights will help determine which parameters should be incorporated into the AI model's prompt. The parameters will be categorized into two groups: those related to the bouldering wall and those specific to the climber, ensuring both are beginner-friendly.
A key aspect of this thesis is developing a responsive web application that uses Generative AI to identify bouldering routes. The system will process visual data from climbing walls, analyze route complexity, and suggest beginner-friendly paths tailored to individual users. A database or cloud storage for user profiles and climbing data will be part of the system, along with a frontend for user interaction and a backend for AI processing and data management. Based on physical characteristics, AI models will be trained to recognize holds, generate routes, and personalized recommendations. However, the development approach remains flexible, as technologies and requirements may evolve throughout the implementation process. Its effectiveness will be evaluated by comparing AI-generated recommendations with established solutions and expert assessments.
This study contributes to the integration of AI and web engineering in sports technology by developing a structured approach for AI-powered climbing route recommendations. Additionally, the research establishes key parameters for AI-powered route optimization, offering a foundation for future advancements in climbing assistance systems and related applications in sports technology.
