Evidence map›Paper›PMID 40567666›Full record

ArticlePeerJ. Computer science2025

Sporting a virtual future: exploring sports and virtual reality patents using deep learning-based analysis.

Jea Woog Lee, Sangmin Song, JungMin Yun, Doug Hyun Han, YoungBin Kim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jea Woog LeeCollege of Sport Sciences, Chung-Ang University, Ansoeng, Republic of South Korea.
Sangmin SongDepartment of Artificial Intelligence, Chung-Ang University, Seoul, Republic of South Korea.
JungMin YunDepartment of Artificial Intelligence, Chung-Ang University, Seoul, Republic of South Korea.
Doug Hyun HanDepartment of Psychiatry, Chung-Ang University Hospital, Seoul, Republic of South Korea.
YoungBin KimGraduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul, Republic of South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigate the convergence of sports and emerging technologies from the Fourth Industrial Revolution, with a focus on virtual reality (VR) applications. Using patent big data, we introduce SportsBERT, a bidirectional encoder representation from transformers (BERT)-based algorithm tailored for enhanced natural language processing in sports-related knowledge-based documents. Through topic modeling, we extract key themes and clusters from sports-related VR patents, providing insights into the knowledge structure and technological trends in VR applications for sports. Our analysis identifies key drivers of technological advancement, including spatial hardware, tactile human-computer interactions, aerobic exercise, rehabilitation, and swing sports. Additionally, we highlight challenges such as the high cost and usability limitations of current VR devices. This study presents the first deep learning-based topic modeling approach specialized for sports patents and offers a comprehensive roadmap for current developments and future trajectories in VR sports technologies.

Indexed as

BERTDeep learningNatural language processingSports patentTopic modelingVirtual reality

Identifiers

PMID40567666
PMCPMC12192641

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.