The DOI matches its Crossref record; 146 of the 148 references it cites were found in a registry or an index.
Control strategies for effective robot assisted gait rehabilitation: The state of art and future prospects
Abstract
A large number of gait rehabilitation robots, together with a variety of control strategies, have been developed and evaluated during the last decade. Initially, control strategies applied to rehabilitation robots were adapted from those applied to traditional industrial robots. However, these strategies cannot optimise effectiveness of gait rehabilitation. As a result, researchers have been investigating control strategies tailored for the needs of rehabilitation. Among these control strategies, assisted-as-needed (AAN) control is one of the most popular research topics in this field. AAN training strategies have gained the theoretical and practical evidence based backup from motor learning principles and clinical studies. Various approaches to AAN training have been proposed and investigated by research groups all around the world. This article presents a review on control algorithms of gait rehabilitation robots to summarise related knowledge and investigate potential trends of development. There are existing review papers on control strategies of rehabilitation robots. The review by Marchal-Crespo and Reinkensmeyer (2009) had a broad cover of control strategies of all kinds of rehabilitation robots. Hussain et al. (2011) had specifically focused on treadmill gait training robots and covered a limited number of control implementations on them. This review article encompasses more detailed information on control strategies for robot assisted gait rehabilitation, but is not limited to treadmill based training. It also investigates the potential to further develop assist-as-needed gait training based on assessments of patients' ability. In this paper, control strategies are generally divided into the trajectory tracking control and AAN control. The review covers these two basic categories, as well as other control algorithm and technologies derived from them, such as biofeedback control. Assessments on human gait ability are also included to investigate how to further develop implementations based on assist-as-needed concept. For the consideration of effectiveness, clinical studies on robotic gait rehabilitation are reviewed and analysed from the viewpoint of control algorithm.
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“By contrast, the AAN strategy suggests that the assistive devices only supply as much effort as a patient needs to accomplish training tasks by assessing his or her performance in real-time [8].”
From Recent developments and challenges of lower extremity exoskeletons (2016) · cites this paper as [8]
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Cited by
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Recent developments and challenges of lower extremity exoskeletons
Chen, B., Ma, H., Qin, L.-Y., Gao, F., Chan, K.-M., Law, S.-W., Qin, L., & Liao, W.-H. (2016) · Journal of Orthopaedic Translation · doi:10.1016/j.jot.2015.09.007 · Cited by 501
“In this situation, therapists can concentrate more on analyzing the patient’s gait performance so as to provide more effective rehabilitation [8].”
BackgroundRecent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation
Meng, W., Liu, Q., Zhou, Z., Ai, Q., Sheng, B., & Xie, S. Q. (2015) · Mechatronics · doi:10.1016/j.mechatronics.2015.04.005 · Cited by 464
Self-citationMeuleman, J., van Asseldonk, E. H. F., van Oort, G., Rietman, H. S., & van der Kooij, H. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2511448 · Cited by 172
Academic Review and Perspectives on Robotic Exoskeletons
Bao, G., Pan, L., Fang, H., Wu, X., Yu, H., Cai, S., Yu, B., & Wan, Y. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2944655 · Cited by 135
A wearable exoskeleton suit for motion assistance to paralysed patients
Chen, B., Zhong, C.-H., Zhao, X., Ma, H., Guan, X., Li, X., Liang, F., Cheng, J. C. Y., Qin, L., Law, S. W., et al. (2017) · Journal of Orthopaedic Translation · doi:10.1016/j.jot.2017.02.007 · Cited by 110
Metadata
- Authors
- Cao, J., Xie, S. Q., Das, R., & Zhu, G. L.
- Affiliations
- University of Auckland (NZ)
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Xie, Sheng Quan
- Venue
- Medical Engineering & Physics
- Publisher
- IOP Publishing
- Published
- 2014-09-07
- Volume
- 36
- Issue
- 12
- Pages
- 1555-1566
- ISSN
- 1350-4533 (ISSN-L)
- 1873-4030
- OpenAlex
- W2090854043
- Cited by
- 153
- References
- 148
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 8 · 2023: 17 · 2022: 16 · 2021: 16 · 2020: 12 · 2019: 17 · 2018: 19 · 2017: 13 · 2016: 8 · 2015: 6
- FWCI
- 0.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Medical Engineering & Physics (publishedVersion)
- PubMed (publishedVersion)
- RMIT Research Repository (RMIT University Library) (submittedVersion)
- Figshare (submittedVersion)
- Funders
- University of Auckland Doctoral Scholarship
- Auckland Medical Research Foundation
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Locomotion and Control
- Keywords
- assist-as-needed control, trajectory tracking control, biofeedback control, gait analysis, treadmill gait training, motor learning
- MeSH terms
- Animals, Clinical Trials as Topic, Gait, Humans, Robotics, Gait Disorders, Neurologic
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