Using Machine Learning for move sequence visualization and generation in climbing

2025-03-01

AI Summary

This study addresses two core challenges in sport climbing: (1) visual assessment of climbing movement sequences and (2) prediction of dynamic movement sequences solely from simplified hold sequences. We propose the first Transformer-based framework for climbing movement modeling, jointly encoding 2D hold coordinates and topological relationships to enable end-to-end generation of dynamic movements from static hold configurations. Three Transformer variants are designed, and an interactive visualization frontend—supporting real-time annotation and feedback—is developed. Experiments demonstrate the feasibility of sequence-level movement generation, and the system has been deployed as an interactive analytical tool. This work establishes a novel paradigm for intelligent climbing analysis grounded in deep sequence modeling, providing a methodological foundation and benchmark framework for future research on high-precision movement prediction, route difficulty quantification, and training assistance.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Visualizing climbing move sequences using Machine Learning
• Predicting climbing moves from hold sequences with Transformer models
• Developing tools for evaluating boulder climbing sequences

Innovation

• Machine Learning for climbing move visualization
• Transformer models for sequence prediction
• Visualization tool for boulder move evaluation