Evidence map›Paper›PMID 42564242›Full record

ArticleFrontiers in bioengineering and biotechnology2026

Multi-dimensional dynamic visualization of spatiotemporal data for tactical analysis in table tennis.

Jianwei Guo, Qingyun Huang, Lei Gao, Xingdong Zhou, Lei Qian, Jing Sun, Dandan Xiao

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Article in Frontiers in bioengineering and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Jianwei GuoSchool of Athletic Performance, Shanghai University of Sport, Shanghai, China.
Qingyun HuangSchool of Artificial Intelligence and Computer Science,North China University of Technology, Beijing, China.
Lei GaoBeijing Chuangxin Zhijiu Technology Co., Ltd, Beijing, China.
Xingdong ZhouThe Faculty of Sports Science, Ningbo University, Ningbo, China.
Lei QianPhysical Education School, Xian Technological University, Xian, China.
Jing SunSchool of Artificial Intelligence and Computer Science,North China University of Technology, Beijing, China.
Dandan XiaoCollege of Sports and Health, Shandong Sport University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Table tennis is a high-speed racket sport that generates complex technical and tactical data, including stroke sequences, landing-point distributions, shot techniques, player names, and point outcomes. Traditional statistical charts and two-dimensional visualizations are often limited in representing multidimensional categorical relationships and the dynamic evolution of landing patterns across rallies. This study therefore aims to develop a visual analytics framework for exploratory post-match technical and tactical diagnosis in table tennis. Methods: A multimodal visual analytics framework was developed by integrating a cluster-enhanced parallel coordinates module with a stroke-sequence-oriented spatiotemporal cube module. In the parallel coordinates module, K-Modes clustering was used to aggregate categorical stroke-event records into representative tactical profiles, thereby reducing line occlusion and revealing co-occurrence patterns among player name, stroke number, stroke position, landing position, shot technique, and point outcome. In the spatiotemporal cube module, landing points within each rally were modeled as discrete landing-point sequences, and sub-trajectory clustering was applied to identify recurring landing-path patterns and the evolution of landing hotspots across stroke sequences. Two independent match cases with reliability-checked annotations were used to evaluate the two modules according to their respective analytical objectives. In addition, five experts assessed the system using a seven-point Likert scale. Results: The results indicated that the cluster-enhanced parallel coordinates module improved the readability of multidimensional categorical data and supported the identification and interpretation of player-specific tactical profiles. The spatiotemporal cube module revealed recurring landing-path patterns and stroke-sequence-based changes in landing-point distributions. The expert evaluation further supported the analytical usefulness and interpretability of the proposed framework for post-match technical and tactical analysis. Discussion: By combining categorical tactical-pattern analysis with spatiotemporal landing-sequence analysis, the proposed framework provides a domain-adapted approach for examining both multidimensional technical-tactical relationships and the dynamic evolution of landing patterns in table tennis. The framework supports exploratory tactical diagnosis and offers methodological implications for the visualization and analysis of spatiotemporal data in racket sports.

Indexed as

cluster analysisparallel coordinatesspatiotemporal cubespatiotemporal datatable tennisvisual analytics

Identifiers

PMID42564242
PMCPMC13442433

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.