Chinese Journal of Tissue Engineering Research ›› 2022, Vol. 26 ›› Issue (26): 4224-4233.doi: 10.12307/2022.828

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Knowledge graph analysis of virtual reality technology in stroke treatment

Zou Mengyao1, Wang Junhua2, Qin Pengfei3, Zhong Weihua2   

  1. 1School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, Hubei Province, China; 2Department of Rehabilitation, Taihe Hospital Affiliated to Hubei University of Medicine, Shiyan 442000, Hubei Province, China; 3School of Sports Science, Fujian Normal University, Fuzhou 350108, Fujian Province, China
  • Received:2021-08-16 Accepted:2021-09-15 Online:2022-09-18 Published:2022-03-09
  • Contact: Wang Junhua, MD, Professor, Master’s supervisor, Chief physician, Department of Rehabilitation, Taihe Hospital Affiliated to Hubei University of Medicine, Shiyan 442000, Hubei Province, China
  • About author:Zou Mengyao, Master candidate, Junior therapist, School of Biomedical Engineering, Hubei University of Medicine, Shiyan 442000, Hubei Province, China
  • Supported by:
    a grant from the Department of Rehabilitation in Taihe Hospital Affiliated to Hubei Uneversity of Medicine, No. 2020JJXM111

Abstract: BACKGROUND: Stroke has become one of the thorny diseases facing the world due to its high morbidity, high mortality, high disability rate, and high recurrence rate. However, traditional rehabilitation methods, characterized by slow effect, low efficiency, and dull training, elicit little effects in the treatment of stroke. With the development of science and technology, virtual reality technology which has the characteristics of simplicity, high efficiency, high security, strong interest, low price, and real-time feedback can solve the imbalance of doctor-patient ratio, promote the development of remote rehabilitation medicine, and has extremely important practical significance for the rehabilitation of stroke patients.
OBJECTIVE: To study the research hotspots and progress of virtual reality technology in stroke treatment using Citespace V knowledge graph software, and to clarify the structural model of this field and predict the possible development trends of virtual reality technology worldwide in the future. 
METHODS: A total of 1 374 core articles addressing virtual reality technology published since 2007 were retrieved from Web of ScienceTM, CNKI, CSSCI, and CSCD database and used as data sources. Citespace V knowledge graph software was used to analyze the research status of virtual reality technology in stroke treatment. In addition, the domestic and foreign differences in disciplines, countries, authors, institutions, and research hotspots were compared. Then, the possible development trend in the future was predicted.
RESULTS AND CONCLUSION: The foreign countries have formed a multi-disciplinary and multi-domain comprehensive group, focusing on neuroscience, computer science, and biomedical engineering. Domestic disciplines are relatively monotonous, and no obvious discipline group has formed. There is a network of close exchanges and cooperation that has formed between foreign countries/regions, authors, and institutions, forming a high school mentality of countries, authors, and institutions. The cooperation among domestic authors, groups and institutions is poor, with geographical restrictions, and there is a lack of influential academic groups and institutions. There are differences in research contents at home and abroad, and there have been four development stages of this technology in foreign countries. The possible trends of future research abroad are the research, development and evaluation of remote rehabilitation medicine, robot-assisted therapy, and virtual rehabilitation system, highlighting the screening of virtual rehabilitation therapy. The possible future research topics in China may be the effect and evaluation of the combined treatment of specific parts with virtual reality technology and other methods, and the introduction and development of advanced technology.

Key words: stroke,  , virtual reality technology,  , rehabilitation,  , knowledge graph, research hotspots

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