Rehabilitation robots, as representative advanced modern rehabilitation devices, are automatically operated machines used for improving the motor functions of patients. Research on rehabilitation robots is typically multidisciplinary research involving technical engineering, clinical medicine, neural science, and other disciplines. Understanding the emerging trends and high-impact publications is important for providing an overview of rehabilitation robot research for interested researchers. Bibliometric analysis is the use of statistical methods to analyze publications over a period of time, which can provide visual insights into the relationships between studies and their publications. In this study, we used “rehabilitation robot *” as a topic term to collect 3527 papers from Web of Science in 127 subject categories published between 2000 and 2019. Rehabilitation robot research has increased rapidly over the past 20 years, 10 key clusters of which were analyzed in this narrative review: improving functional ability after stroke, spinal cord injury, universal haptic drive, robotic-assisted treadmill therapy, treadmill training, increasing productivity, custom-designed haptic training, physical treatment strategies, arm movement therapy, and rehabilitation robotics. Based on this database, we constructed co-citation and co-occurrence networks that were characterized by betweenness centrality values of more than 0.08 and citation bursts with strengths of more than 23, thereby visualizing the emerging trends in the research of rehabilitation robots.
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