OptiPath: A Multidimensional Educational Platform for Enhancing Technical Skills in Alpine Skiing

Denisa-Iulia Brus () and Răzvan Sandu Enoiu ()
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Denisa-Iulia Brus: Interdisciplinary Doctoral School, Transilvania University of Brasov, Brasov 500036, Romania
Răzvan Sandu Enoiu: Department of Motor Performance, Transilvania University of Brasov, Brasov 500036, Romania

Revista romaneasca pentru educatie multidimensionala - Journal for Multidimensional Education, 2025, vol. 17, issue 2, 344-361

Abstract: OptiPath is a multidimensional desktop application designed to enhance alpine skiing technique by integrating video analysis, artificial intelligence, and machine learning. The platform enables users to upload skiing footage, from which it performs frame-by-frame detection of gates and skier trajectory, allowing for a comparative analysis between the actual and optimal skiing paths. This feedback mechanism supports athletes and coaches in identifying technical execution errors and improving performance outcomes. Developed in Python, the system utilizes object detection and image processing algorithms to deliver accurate visualizations, all without requiring an internet connection—making it particularly suited for training in remote environments. Tested under real-world giant slalom conditions, OptiPath demonstrated both reliability and ease of use, offering a lightweight, data- private solution that does not rely on user accounts or cloud storage. Its minimalist design and efficient processing capabilities make it accessible across a range of hardware configurations. By bridging traditional coaching methods with modern technological tools, OptiPath provides a precise, adaptable, and user-friendly means of technical evaluation, ultimately contributing to more effective skill development and injury prevention in alpine skiing.

Keywords: alpine skiing; artificial intelligence; image processing; ideal path; algorithm; mathematical model; performance analysis. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:lum:rev1rl:v:17:y:2025:i:2:p:344-361

DOI: 10.18662/rrem/17.2/985

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