Manuscript received December 2, 2024; revised December 17, 2024; accepted January 7, 2025; published January 22, 2025.
Abstract—Fencing, a sport rich in history and strategic depth, presents unique challenges for data analysis. Despite its complexity, traditional visualization tools often fall short in capturing the intricate dynamics of fencing. This paper takes a closer look at the limitations of these tools and explores innovative visualization techniques from other sports that could be adapted to better analyze fencing techniques and strategies. Deep learning technologies hold particular promise for fencing analysis. Leveraging these advanced tools could uncover new insights and enhance our understanding of the sport. The potential benefits are significant, with improved visualization and analysis capabilities poised to revolutionize the way fencers and coaches train and compete. This review highlights the pressing need for tailored visualization solutions that can keep pace with the demands of fencing. By developing more effective analytical tools, we can empower fencers and coaches to make data-driven decisions, optimize their performance, and gain a competitive edge.
Keywords—fencing analysis, visualization, deep learning, data-driven decisions, performance optimization
Cite: Ziqian Wang, "Technical and Tactical Analysis in Fencing: A Review of Current Visualization Technologies,"
International Journal of Engineering and Technology, vol. 17, no. 1, pp. 19-26, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
