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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.
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.
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.
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 (
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.
The past 2 decades have seen rapid, vast development of robots for rehabilitation. Rehabilitation robots are representative of advanced modern rehabilitation devices; they are automatically operated machines used to treat patients with impaired motor function [
Literature reviews provide researchers with a comprehensive understanding of particular areas of research [
Recently, improvements in computer and information science have enhanced bibliometric analysis and allowed an intensive interpretation of emerging trends in single and multiple research fields [
In this study, we performed a bibliometric analysis, including a co-occurrence and co-citation network analysis, of research conducted between 2001 and 2020 on robots for rehabilitation. Our aim was to understand developments in this research field and identify emerging trends.
The bibliometric data used in this study were derived from scientific literature indexed by the Web of Science (WoS) Core Collection as of July 28, 2022. A comprehensive search strategy was used to meet the requirements for data coverage. This strategy involved both index terms and keywords, including truncation, proximity, and phrases. Terms such as “rehabilitation robot” were searched for as “rehabili* robot*” to identify all related terms. Document types were limited to “article” and “meeting,” excluding documents classified as “review” papers. The records included basic attributes of the documents, such as publication time, author, institution, country, and cited references; these were used to form a database that was used for the subsequent analysis.
In this paper, CiteSpace, a Java application developed by Chen [
A Mann-Kendall test was used to assess whether the literature data, including publications and citations, increased year over year, and whether trends were statistically significant. For each comparison pair (publications or citations in 2 adjacent years), we assigned a score of +1 if the latter value was greater than the former value and a score of –1 if the latter value was lower than the former value. All scores were then summed to calculate the test statistic,
A co-occurrence analysis was performed to observe the relationship between shared words in the literature. The frequency of word occurrence is associated with the underlying themes. In this analysis, a co-occurrence clustering network was generated to examine changes in specific topics in research on the use of robots in rehabilitation [
The co-occurrence network and co-citation networks were mainly characterized by 2 metrics, betweenness centrality (BC) and burst strength (BS). BC was used to identify pathways between different thematic clusters. This computation was based on a fast algorithm introduced by Brandes [
After data retrieval, cleaning, and deduplication, we retrieved a total of 9287 publications and 110,619 cited references in the field of research on robots for rehabilitation that were published from 2001 to 2020; we used these data to form a database (
Web of Science–indexed publications from 2001 to 2020 on the topic of robots for rehabilitation and citations in these publications.
Each publication indexed in the WoS is assigned to one or more categories. Based on the classification of categories in the database [
Co-occurrence network of publications on rehabilitation robots published from 2001 to 2020. CC: co-citation; E: edge.; e: equivalency; L/N: link/node; LBY: look back years; LRF: link retaining factor; N: node; Q: modality; S: weighted mean silhouette.
Prominent co-occurrence categories in publications related to rehabilitation robots (2001-2020).
Publications, n | Burst strength | Betweenness centrality | Categories |
3334 | N/Aa | 0.14 |
|
2498 | N/A | 0.16 |
|
2285 | 26.01 | 0.21 |
|
1822 | N/A | 0.25 |
|
1557 | N/A | 0.06 |
|
1412 | 19.36 | 0.09 |
|
830 | N/A | 0.24 |
|
716 | N/A | 0.07 |
|
454 | N/A | 0.02 |
|
447 | N/A | 0.04 |
|
416 | 11.54 | 0.06 |
|
333 | 20.02 | 0.27 |
|
322 | N/A | 0.07 |
|
278 | N/A | 0.04 |
|
219 | N/A | 0.07 |
|
219 | N/A | 0.03 |
|
193 | N/A | 0.05 |
|
185 | 6.13 | 0 |
|
179 | N/A | 0.02 |
|
172 | N/A | 0.01 |
|
156 | 8.97 | 0 |
|
145 | N/A | 0.04 |
|
127 | N/A | 0.12 |
|
114 | N/A | 0.01 |
|
102 | N/A | 0.01 |
|
95 | 10.79 | 0.01 |
|
85 | N/A | 0.02 |
|
81 | N/A | 0 |
|
78 | 14.93 | 0.12 |
|
78 | 4.00 | 0.03 |
|
aN/A: not applicable.
Based on the literature data, a network of co-citation clusters was constructed and visualized (
Further, we used the timeline view to visualize the evolution of the publications (
Network of co-citation clusters of publications on rehabilitation robots published from 2001 to 2020. CC: co-citation; E: edge.; e: equivalency; L/N: link/node; LBY: look back years; LRF: link retaining factor; N: node; Q: modality; S: weighted mean silhouette.
Prominent co-citation clusters related to rehabilitation robots in research published from 2001 to 2020.
Cluster | Size, n | Silhouette | Year | Duration | Labels |
0 | 186 | 0.877 | 2017 | 2009 to 2019 | soft robotic; hand rehabilitation; chronic stroke; stroke survivor; stroke patient; pilot study; robotic rehabilitation; daily living; exoskeleton robot; gait rehabilitation |
1 | 122 | 0.854 | 2008 | 2001 to 2012 | following stroke; chronic stroke; stroke patient; pilot study; chronic stroke patient; robotic device; stroke rehabilitation; motor learning; motor recovery |
2 | 98 | 0.892 | 2011 | 2003 to 2013 | robotic-assistant gait rehabilitation; active participation; gait training; post-stroke early neurorehabilitation; cable-driven locomotor training system; human spinal cord injury; packet loss |
3 | 83 | 0.967 | 2002 | 1996 to 2003 | stroke patient; chronic stroke; rehabilitation system; using actuator; robotic therapy; robot-assisted movement training; conventional therapy technique; follow-up result; potential recovery |
4 | 58 | 1.000 | 2002 | 1996 to 2005 | robotic system; intelligent sweet home; welfare-oriented service; effective intention reading; visual servoing; human-friendly man-machine interaction unit; novel type rehabilitation; wheelchair-based robotic arm; effective intention reading |
5 | 56 | 0.946 | 2005 | 1999 to 2005 | incomplete spinal cord injury; joint kinematics; ankle-foot orthoses; muscle activation; gait training; measuring human training skill; robot control algorithm; robotic-assisted treadmill training |
Timeline view of co-citation clusters of publications from 2001 to 2020 on rehabilitation robots. The publication year is displayed horizontally and prominent labels identified from each cluster are displayed vertically. CC: co-citation; E: edge.; e: equivalency; L/N: link/node; LBY: look back years; LRF: link retaining factor; N: node; Q: modality; S: weighted mean silhouette.
BC and BS are two important metrics in the co-citation analysis.
Top 10 publications with highest betweenness centrality.
Rank | Betweenness centrality | Authors | Year | Publication title |
1 | 0.15 | Lum et al [ |
2002 | Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke |
2 | 0.12 | Prange et al [ |
2006 | Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke |
3 | 0.10 | Lo et al [ |
2010 | Robot-assisted therapy for long-term upper-limb impairment after stroke |
4 | 0.10 | Song et al [ |
1999 | KARES: Intelligent wheelchair-mounted robotic arm system using vision and force sensor |
5 | 0.07 | Krebs et al [ |
1998 | Robot-aided functional imaging: application to a motor learning study |
6 | 0.06 | Reinkensmeyer et al [ |
1999 | Guidance-based quantification of arm impairment following brain injury: A pilot study |
7 | 0.05 | Maciejasz et al [ |
2014 | A survey on robotic devices for upper limb rehabilitation |
8 | 0.05 | Fasoli et al [ |
2003 | Effects of robotic therapy on motor impairment and recovery in chronic stroke |
9 | 0.04 | Ferraro et al [ |
2003 | Robot-aided sensorimotor arm training improves outcome in patients with chronic stroke |
10 | 0.04 | Stienen et al [ |
2009 | Self-aligning exoskeleton axes through decoupling of joint rotations and translations |
Top-10 publications with highest burst strength.
Rank | Burst strength | Authors | Year | Duration | Publication title |
1 | 79.07 | Maciejasz et al [ |
2014 | 2015 to 2020 | A survey on robotic devices for upper limb rehabilitation |
2 | 42.36 | Polygerinos et al [ |
2015 | 2016 to 2020 | Soft robotic glove for combined assistance and at-home rehabilitation |
3 | 39.50 | Meng et al [ |
2015 | 2017 to 2020 | Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation |
4 | 34.94 | Klamroth-Marganska et al [ |
2014 | 2015 to 2020 | Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial |
5 | 33.88 | Young et al [ |
2017 | 2018 to 2020 | State-of-the-art and future directions for lower limb robotic exoskeletons |
6 | 33.38 | Yan et al [ |
2015 | 2016 to 2020 | Review of assistive strategies in powered lower-limb orthoses and exoskeletons |
7 | 30.65 | Veerbeek et al [ |
2017 | 2018 to 2020 | Effects of robot-assisted therapy for the upper limb after stroke: a systematic review and meta-analysis |
8 | 29.14 | Tucker et al [ |
2015 | 2017 to 2020 | Control strategies for active lower extremity prosthetics and orthotics: a review |
9 | 27.42 | Zhang et al [ |
2017 | 2018 to 2020 | Human-in-the-loop optimization of exoskeleton assistance during walking |
10 | 25.58 | Awad et al [ |
2017 | 2018 to 2020 | A soft robotic exosuit improves walking in patients after stroke |
In this study, we performed a bibliometric analysis of research on rehabilitation robotics published in the last 2 decades; we obtained insights on emerging trends in this research field. Our analysis indicates that the emergence of new technologies, such as virtual reality, brain-computer interfaces, and intelligent sensing, is advancing the development of rehabilitation robotics. Robots with a flexible structure are currently attracting great attention in robot design.
Literature (9287 publications in total) retrieved from the WoS database between 2001 and 2020 shows a continuous increase in research interest in rehabilitation robotics. We excluded review papers from the literature data because they might have interfered with the bibliometric analysis; compared with original-research papers, review articles are more likely to be identified as prominent publications because they are usually highly cited by other researchers. However, most review papers are not associated with technical keywords. Even if some review papers provide technical keywords, they lag behind the publication date of the paper. Consequently, specific technologies identified as being important and having an increasing trend likely do not match the period of their actual emergence. For this reason, we excluded review papers from our document retrieval.
Based on the literature data, we performed co-occurrence and co-citation analyses to characterize emerging trends in research on rehabilitation robots. The co-occurrence analysis of categories showed that a majority of articles on rehabilitation robots are published in fields related to engineering, such as robotics, electrical and electronic engineering, and biomedical engineering. The development of rehabilitation robotics has been going on for many years, and technological progress will continue in the coming decades. Newly emerging engineering technologies (eg, virtual reality, brain-computer interfaces, and intelligent sensing) are key drivers of advances in the development of rehabilitation robotics [
The co-citation analysis identified 169 clusters, allowing us to characterize and interpret the structure and dynamics of co-citation in research on rehabilitation robots. Specifically, we identified 7 prominent clusters that represent important research themes. The largest cluster (cluster 0;
Limitations of this bibliometric analysis include, first, that it was based on literature data retrieved only from the WoS database. This database might not include publications that were present in other databases, such as PubMed, Google Scholar, and Scopus, meaning that they were missing from our analysis. The main reason for selecting the WoS was that this database is designed to allow citation analysis and provides more extensive information [
This study identified prominent literature on the use of robots in rehabilitation through bibliometric analysis. We visualized and characterized co-occurrence and co-citation networks of publications in this research field, providing insights into the characteristics of the research and emerging trends over the last 2 decades. Our co-occurrence analysis showed that the emergence of new engineering technologies (eg, virtual reality, brain-computer interfaces, and intelligent sensing) advances the development of rehabilitation robotics. Our co-citation analysis indicates that flexible-structure robots are currently gaining wide attention for rehabilitative applications. Rehabilitation robots have been used most extensively in the treatment of chronic stroke. It is foreseeable that research on rehabilitation robots in the coming years will enjoy explosive growth that will promote extensive applications in improving motor function and quality of life in human patients.
Log-likelihood ratio (LLR) algorithm.
The burst detection algorithm and its interpretation.
Database.
Top 20 cited references in cluster #0.
Top 20 cited references in cluster #1.
Top 20 cited references in cluster #2.
betweenness centrality
burst strength
log-likelihood ratio
modality
mean silhouette
Web of Science
This work was supported by the National Natural Science Foundation of China (T2288101, U20A20390, and 11827803) and the National Key Research and Development Plan of China (2020YFC2004200). We appreciate Dr Min Tang’s contribution to the statistical analysis in the revised manuscript.
The data sets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.
None declared.