Research Status and Emerging Trends in Virtual Reality Rehabilitation: Bibliometric and Knowledge Graph Study
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) technology has been widely used in rehabilitation training because of its immersive, interactive, and imaginative features. A comprehensive bibliometric review is required to help researchers focus on future directions based on the new definitions of VR technologies in rehabilitation, which reveal new situations and requirements. OBJECTIVE: Herein, we aimed to summarize effective research methods for and potential innovative approaches to VR rehabilitation by evaluating publications from various countries to encourage research on efficient strategies to improve VR rehabilitation. METHODS: The SCIE (Science Citation Index Expanded) database was searched on January 20, 2022, for publications related to the application of VR technology in rehabilitation research. We found 1617 papers, and we created a clustered network, using the 46,116 references cited in the papers. CiteSpace V (Drexel University) and VOSviewer (Leiden University) were used to identify countries, institutions, journals, keywords, cocited references, and research hot spots. RESULTS: A total of 63 countries and 1921 institutes have contributed publications. The United States of America has taken the leading position in this field; it has the highest number of publications; the highest h-index; and the largest collaborative network, which includes other countries. The reference clusters of SCIE papers were divided into the following nine categories: kinematics, neurorehabilitation, brain injury, exergames, aging, motor rehabilitation, mobility, cerebral palsy, and exercise intensity. The research frontiers were represented by the following keywords: video games (2017-2021), and young adults (2018-2021). CONCLUSIONS: Our study comprehensively assesses the current research state of VR rehabilitation and analyzes the current research hot spots and future trends in the field, with the aims of providing resources for more intensive investigation and encouraging more researchers to further develop VR rehabilitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.228 | 0.319 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".