Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020

Abstract

Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research. Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words. Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain-computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention. Conclusions: At present, the brain-computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.

Keywords: CiteSpace; artificial intelligence; bibliometric analysis; machine learning; rehabilitation robot; trend.

Figure 1

Flowchart of data filtration processing and excluding publications. WoSCC, Web of Science Core Collection.

Figure 2

Bibliometric analysis of WoS core database output. From 2010 to 2020, the number of published publications by different countries on rehabilitation robot research has changed over the years.

Figure 3

2010–2020 rehabilitation robot literature research hotspot distribution.

Figure 4

Bibliometric analysis of the cooperation of countries in the field of rehabilitation robot research.

Figure 5

Co-occurrence atlas of rehabilitation robot research institutions. The bigger the circle, the more it appears. Lines refer to the connections between institutions. The thicker the lines, the closer the connections.

Figure 6

Author and cooperative relationship. The bigger the circle, the more it appears. Lines refer to the connections between authors. The thicker the lines, the closer the connections.

Figure 7

Co-occurrence spectrum of high-frequency keywords in rehabilitation robot field. The cross represents keywords; the larger the cross, the thicker the cross, the higher the frequency of keywords; the line refers to the connection between keywords; the thicker the line, the closer the connection.

Figure 8

Cited cluster analysis showing the nine main research areas.

Figure 9

Time dynamic evolution of keywords.