Characteristics and Emerging Trends in Research on Rehabilitation Robots from 2001 to 2020: Bibliometric Study

被引:11
作者
Zhang, Ying [1 ,2 ,3 ,4 ]
Liu, Xiaoyu [1 ,2 ,3 ,5 ]
Qiao, Xiaofeng [1 ,2 ,3 ]
Fan, Yubo [1 ,2 ,3 ,5 ]
机构
[1] Beihang Univ, Sch Biol Sci & Med Engn, Bldg 5,37 Xueyuan Rd, Beijing 100191, Peoples R China
[2] Beihang Univ, Key Lab Biomech & Mechanobiol, Minist Educ, Beijing, Peoples R China
[3] Beihang Univ, Beijing Adv Innovat Ctr Biomed Engn, Beijing, Peoples R China
[4] Beijing Acad Sci & Technol, Inst Informat & Artificial Intelligence Technol, Beijing, Peoples R China
[5] Beihang Univ, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
rehabilitation robot; bibliometric analysis; interdisciplinary research; co -occurrence analysis; co -citation analysis; rehabilitation; THERAPY; ARM; RECOVERY; SCIENCE; STROKE; EXOSKELETON; PERSPECTIVE; IMPAIRMENT; STRATEGIES; ASSISTANCE;
D O I
10.2196/42901
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: The past 2 decades have seen rapid development in the use of robots for rehabilitation. Research on rehabilitation robots involves interdisciplinary activities, making it a great challenge to obtain comprehensive insights in this research field.Objective: We performed a bibliometric study to understand the characteristics of research on rehabilitation robots and emerging trends in this field in the last 2 decades.Methods: Reports on the topic of rehabilitation robots published from January 1, 2001, to December 31, 2020, were retrieved from the Web of Science Core Collection on July 28, 2022. Document types were limited to "article" and "meeting" (excluding the "review" type), to ensure that our analysis of the evolution over time of this research had high validity. We used CiteSpace to conduct a co-occurrence and co-citation analysis and to visualize the characteristics of this research field and emerging trends. Landmark publications were identified using metrics such as betweenness centrality and burst strength. Results: Through data retrieval, cleaning, and deduplication, we retrieved 9287 publications and 110,619 references cited in these publications that were on the topic of rehabilitation robots and were published between 2001 and 2020. Results of the Mann-Kendall test indicated that the numbers of both publications (P<.001; St=175.0) and citations (P<.001; St=188.0) related to rehabilitation robots exhibited a significantly increasing yearly trend. The co-occurrence results revealed 120 categories connected with research on rehabilitation robots; we used these categories to determine research relationships. The co-citation results identified 169 co-citation clusters characterizing this research field and emerging trends in it. The most prominent label was "soft robotic technology" (the burst strength was 79.07), which has become a topic of great interest in rehabilitative recovery for both the upper and lower limbs. Additionally, task-oriented upper-limb training, control strategies for robot-assisted lower limb rehabilitation, and power in exoskeleton robots were topics of great interest in current research.Conclusions: Our work provides insights into research on rehabilitation robots, including its characteristics and emerging trends during the last 2 decades, providing a comprehensive understanding of this research field.
引用
收藏
页数:14
相关论文
共 80 条
  • [1] 4.2 Clustering, 4 2 CLUST CITESPACE
  • [2] A study of scientometric methods to identify emerging technologies via modeling of milestones
    Abercrombie, Robert K.
    Udoeyop, Akaninyene W.
    Schlicher, Bob G.
    [J]. SCIENTOMETRICS, 2012, 91 (02) : 327 - 342
  • [3] An information-theoretic perspective of tf-idf measures
    Aizawa, A
    [J]. INFORMATION PROCESSING & MANAGEMENT, 2003, 39 (01) : 45 - 65
  • [4] Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
    AlRyalat, Saif Aldeen S.
    Malkawi, Lna W.
    Momani, Shaher M.
    [J]. JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2019, (152):
  • [5] A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke
    Ang, Kai Keng
    Chua, Karen Sui Geok
    Phua, Kok Soon
    Wang, Chuanchu
    Chin, Zheng Yang
    Kuah, Christopher Wee Keong
    Low, Wilson
    Guan, Cuntai
    [J]. CLINICAL EEG AND NEUROSCIENCE, 2015, 46 (04) : 310 - 320
  • [6] A soft robotic exosuit improves walking in patients after stroke
    Awad, Louis N.
    Bae, Jaehyun
    O'Donnell, Kathleen
    De Rossi, Stefano M. M.
    Hendron, Kathryn
    Sloot, Lizeth H.
    Kudzia, Pawel
    Allen, Stephen
    Holt, Kenneth G.
    Ellis, Terry D.
    Walsh, Conor J.
    [J]. SCIENCE TRANSLATIONAL MEDICINE, 2017, 9 (400)
  • [7] Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review
    Baniqued, Paul Dominick E.
    Stanyer, Emily C.
    Awais, Muhammad
    Alazmani, Ali
    Jackson, Andrew E.
    Mon-Williams, Mark A.
    Mushtaq, Faisal
    Holt, Raymond J.
    [J]. JOURNAL OF NEUROENGINEERING AND REHABILITATION, 2021, 18 (01)
  • [8] A faster algorithm for betweenness centrality
    Brandes, U
    [J]. JOURNAL OF MATHEMATICAL SOCIOLOGY, 2001, 25 (02) : 163 - 177
  • [9] CO-WORD ANALYSIS AS A TOOL FOR DESCRIBING THE NETWORK OF INTERACTIONS BETWEEN BASIC AND TECHNOLOGICAL RESEARCH - THE CASE OF POLYMER CHEMISTRY
    CALLON, M
    COURTIAL, JP
    LAVILLE, F
    [J]. SCIENTOMETRICS, 1991, 22 (01) : 155 - 205
  • [10] Toward Impactful Collaborations on Computing and Mental Health
    Calvo, Rafael Alejandro
    Dinakar, Karthik
    Picard, Rosalind
    Christensen, Helen
    Torous, John
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2018, 20 (02)