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Journal of Healthcare Engineering
Volume 2018, Article ID 2858294, 12 pages
Review Article

Reviewing Clinical Effectiveness of Active Training Strategies of Platform-Based Ankle Rehabilitation Robots

1School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, China
2Department of Mechanical Engineering, University of Auckland, Auckland 1142, New Zealand
3The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Luoyu Road 1037, Wuhan, China
4School of Mechanical Engineering and School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK

Correspondence should be addressed to Guoli Zhu; moc.361@hw_uhzlg

Received 25 August 2017; Accepted 9 January 2018; Published 20 February 2018

Academic Editor: Maria Lindén

Copyright © 2018 Xiangfeng Zeng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. This review aims to provide a systematical investigation of clinical effectiveness of active training strategies applied in platform-based ankle robots. Method. English-language studies published from Jan 1980 to Aug 2017 were searched from four databases using key words of “Ankle” AND “Robot” AND “Effect OR Improv OR Increas.” Following an initial screening, three rounds of discrimination were successively conducted based on the title, the abstract, and the full paper. Result. A total of 21 studies were selected with 311 patients involved; of them, 13 studies applied a single group while another eight studies used different groups for comparison to verify the therapeutic effect. Virtual-reality (VR) game training was applied in 19 studies, while two studies used proprioceptive neuromuscular facilitation (PNF) training. Conclusion. Active training techniques delivered by platform ankle rehabilitation robots have been demonstrated with great potential for clinical applications. Training strategies are mostly combined with one another by considering rehabilitation schemes and motion ability of ankle joints. VR game environment has been commonly used with active ankle training. Bioelectrical signals integrated with VR game training can implement intelligent identification of movement intention and assessment. These further provide the foundation for advanced interactive training strategies that can lead to enhanced training safety and confidence for patients and better treatment efficacy.