SkillVis: A visualization tool for boxing skill assessment

Hubert P.H. Shum*, He Wang, Shu Lim HO, Taku Komura

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

4 Citations (Scopus)


Motion analysis and visualization are crucial in sports science for sports training and performance evaluation. While primitive computational methods have been proposed for simple analysis such as postures and movements, few can evaluate the high-level quality of sports players such as their skill levels and strategies. We propose a visualization tool to help visualizing boxers' motions and assess their skill levels. Our system automatically builds a graph-based representation from motion capture data and reduces the dimension of the graph onto a 3D space so that it can be easily visualized and understood. In particular, our system allows easy understanding of the boxer's boxing behaviours, preferred actions, potential strength and weakness. We demonstrate the effectiveness of our system on different boxers' motions. Our system not only serves as a tool for visualization, it also provides intuitive motion analysis that can be further used beyond sports science.

Original languageEnglish
Title of host publicationProceedings - Motion in Games 2016
Subtitle of host publication9th International Conference on Motion in Games, MIG 2016
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450345927
Publication statusPublished - 10 Oct 2016
Event9th International Conference on Motion in Games, MIG 2016 - San Francisco, United States
Duration: 10 Oct 201612 Oct 2016

Publication series

NameProceedings - Motion in Games 2016: 9th International Conference on Motion in Games, MIG 2016


Conference9th International Conference on Motion in Games, MIG 2016
Country/TerritoryUnited States
CitySan Francisco

Scopus Subject Areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Dimensionality reduction
  • Information visualization
  • Motion graph


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