SynthesisFrontiers in sports and active living2025
Quantitative analysis and evaluation of research on the application of computer vision in sports since the 21st century.
Synthesis in Frontiers in sports and active living, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A Movement Description Language for Functional Training Exercise Analysis.Journal of functional morphology and kinesiology · 2026Article
- SKS-transformer: multi-scale and direction-aware attention for inertial sensor-based activity recognition.Frontiers in sports and active living · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Integrating computer vision with sports has significantly transformed competitive, educational, and recreational sports practices. A review of the literature in this field is imperative. The purpose of this paper is to reveal the field's temporal, disciplinary, geographic, journal, and collaborative characteristics and summarize research themes and future trends to promote a systematic understanding of the field within the academic community. Methods: To identify research trends, a bibliometric analysis of 1,209 publications retrieved from the Science Citation Index Expanded in the Web of Science core database was conducted. Results: In terms of time series, publications in the field grew slowly until 2014, and publications in the field increased significantly after 2015, with polynomial models predicting 206, 233, and 263 annual publications over the next three years. In terms of disciplinary structure, three frontier disciplines utilized interdisciplinary knowledge provided by four basic disciplines to make cutting-edge breakthroughs. Geospatially, there is a three-way split between China, the United States, and the United Kingdom, while most African countries are not involved in the research. Regarding journal distribution, research in this field was published in five Q2 and four Q1 journals, mainly in computing, with fewer contributions from sports journals. The study identifies five principal research themes: skill optimization, health monitoring and injury prevention, physical performance assessment, tactical analysis and referee assistance, and immersive event experiences. Furthermore, it highlights existing research limitations and outlines directions for future exploration. Conclusions: Computer vision research in sports has shown high explosive growth in recent years. The field is interdisciplinary but lacks collaboration among interdisciplinary research teams. The quality of the journals published is high, but the main focus is on computer-based journals. The theme of research in this field is centered on the fundamental characteristic of serving human beings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.