AI Visual Recognition Technology in Action Difficulty Grading and Referee Assistant System for Freestyle Roller Skating

Jingxiao Wei

Pinnacle Academic Press Proceedings Series, 2026, vol. 11, 132-139

Abstract: With the rapid development and increasing global popularity of freestyle roller skating, the traditional difficulty grading and judging model, which relies predominantly on subjective human judgment, is no longer able to meet the stringent needs of the standardized development of the sport. Subjective evaluations often lead to inconsistencies during high-stakes competitions. Consequently, this paper focuses on the innovative application of artificial intelligence (AI) visual recognition technology within this athletic field, aiming to significantly improve the objectivity, accuracy, and fairness of judging by constructing a robust difficulty grading model and an intelligent referee assistant system. First, this study comprehensively explains the underlying AI visual recognition technology architecture and details the freestyle roller skating action judging rules. It designs a comprehensive motion data collection scheme and an advanced feature engineering process to capture the intricate dynamics of the skaters. Furthermore, it constructs and optimizes a sophisticated difficulty grading model based on deep learning algorithms, specifically tailored for complex spatial-temporal movements. On this foundational basis, a comprehensive referee assistant system equipped with real-time monitoring and automated referee assistance functions is developed. Its practical application effectiveness is rigorously evaluated through both computer simulation and actual live competition tests. The empirical results demonstrate that this proposed technical solution will effectively improve the accuracy of complex motion recognition and ensure the high consistency of grading. Ultimately, this research provides a highly feasible approach for the intelligent development of freestyle roller skating. In addition, corresponding strategic solutions are proposed to address the potential technical challenges encountered in the real-world application.

Keywords: artificial intelligence; visual recognition; deep learning; sports technology; motion analysis; referee system (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://pinnaclepubs.com/index.php/PAPPS/article/view/813/778 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dba:pappsa:v:11:y:2026:i::p:132-139

Access Statistics for this article

More articles in Pinnacle Academic Press Proceedings Series from Pinnacle Academic Press
Bibliographic data for series maintained by Joseph Clark ().