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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Heart Disease and Stroke Statistics—2012 Update: A Report From the American Heart Association
Writing Group Members, Roger, V. L., Go, A. S., Lloyd-Jones, D. M., Benjamin, E. J., Berry, J. D., Borden, W. B., Bravata, D. M., Dai, S., Ford, E. S., et al. (2012) · Circulation · doi:10.1161/cir.0b013e31823ac046 · Cited by 7,333
Introduction to Robotics mechanics and Control
Craig, J. (1986) · Cited by 5,024
Motor Control and Learning: A Behavioral Emphasis
Schmidt, R. A. (1982) · Cited by 4,770
Performance‐Oriented Assessment of Mobility Problems in Elderly Patients
Tinetti, M. E. (1986) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.1986.tb05480.x · Cited by 3,352
Measuring balance in the elderly: preliminary development of an instrument
Berg, K. (1989) · Physiotherapy Canada · doi:10.3138/ptc.41.6.304 · Cited by 2,746
The Barthel ADL Index: A reliability study
Collin, C., Wade, D. T., Davies, S., & Horne, V. (1988) · International Disability Studies · doi:10.3109/09638288809164103 · Cited by 2,740
Functional MRI evidence for adult motor cortex plasticity during motor skill learning
Kami, A., Meyer, G., Jezzard, P., Adams, M. M., Turner, R., & Ungerleider, L. G. (1995) · Nature · doi:10.1038/377155a0 · Cited by 1,883
Technique to improve chronic motor deficit after stroke.
Taub, E., Miller, N. E., Novack, T. A., Cook, E. W., Fleming, W. C., Nepomuceno, C., Connell, J., & Crago, J. E. (1993) · PubMed · Cited by 1,557
The Atlas of Heart Disease and Stroke
Mackay, J., Mensah, G. A., & Mendis, S. (2004) · Medical Entomology and Zoology · Cited by 1,550
Introduction to robotics: Mechanics and control
Rodd, M. G. (1987) · Automatica · doi:10.1016/0005-1098(87)90105-1 · Cited by 1,511
Flagged first, then by citations
2 of the 148 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–154 of 154
Design, Modeling, and Testing—A Compact Variable-Stiffness Actuator for Knee Joint Dimensions
Si, G., Guo, Z., & Zhang, Z. (2025) · Micromachines · doi:10.3390/mi16121365 · Cited by 3
Hong, J.-C., Cheng, H., Hayashi, Y., Yasuda, K., Ohashi, H., & Iwata, H. (2020) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob49111.2020.9224429 · Cited by 3
Effects of Uni- and Bi-Directional Interaction During Dyadic Ankle and Wrist Tracking
Short, M. R., Ludvig, D., Tommaso, F. D., Vianello, L., Perreault, E. J., Küçüktabak, E. B., Hargrove, L. J., Lynch, K., Burdet, E., & Pons, J. L. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3573956 · Cited by 3
Application Issues for Robotics
Wirz, M., & Rupp, R. (2011) · Springer eBooks · doi:10.1007/978-1-4471-2277-7_2 · Cited by 2
Design of a portable ankle robot with reductive-burden layout and self-adjustable posture
Zhou, Y. (2023) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/09544062221145495 · Cited by 2
Adaptive Human-Like Gait Planning for Stair Climbing of Lower Limb Exoskeleton Robots
Chen, Y., Zhang, X., Zou, C., Liang, W., Huang, Z., Huang, R., Wang, Y., & Cheng, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-0783-9_15 · Cited by 2
Николаевич, Б. В., & Егоров, А. В. (2019) · Istrazivanja i projektovanja za privredu · doi:10.5937/jaes17-20367 · Cited by 2
Feedback-Error Learning for Gait Rehabilitation Using a Powered Knee Orthosis: First Advances
Fernandes, P. N., Carvalho, S. P., Figueiredo, J., Moreno, J. C., & Santos, C. P. (2019) · IEEE Portuguese Meeting on Bioengineering · doi:10.1109/enbeng.2019.8692502 · Cited by 2
Gait training algorithm based on inverse dynamics of walking rehabilitation robot, DDgo Pro
Kim, J.-Y., & Kim, J.-Y. (2021) · Intelligent Service Robotics · doi:10.1007/s11370-021-00357-8 · Cited by 2
Design of an infusion set based on pulse control and gravitational potential energy speed regulation
Lu, J. N., Wang, Y., Li, X., Zhang, J., & Chen, L. (2021) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac50856.2021.9390820 · Cited by 2
Rupp, R., & Wirz, M. (2022) · Springer eBooks · doi:10.1007/978-3-031-08995-4_13 · Cited by 2
Algorithm of Personalized Adjustment of the Active-Passive Mechanotherapy Device for the Ankle Joint
Knyazev, A. A., Jatsun, S. F., & Fedorov, A. V. (2023) · International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) · doi:10.1109/icieam57311.2023.10139024 · Cited by 2
A Data-Driven Predictive Model of Individual-Specific Effects of FES on Human Gait Dynamics
Drnach, L., Allen, J. L., Essa, I. A., & Ting, L. H. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794304 · Cited by 2
Bagheri, A., Dorostkar, D., Zakerzadeh, M. R., Sadigh, M. J., & Mahjoob, M. (2019) · RSI International Conference on Robotics and Mechatronics (ICRoM) · doi:10.1109/icrom48714.2019.9071905 · Cited by 2
Research and Simulation of Fuzzy Adaptive PID Control for Upper Limb Exoskeleton Robot
Yu, S., Lu, S., Jiang, Z., Li, Z., & Zhang, Q. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-6135-9_6 · Cited by 1
THE CURRENT CAPABILITIES AND FUTURE POTENTIAL OF ROBOTIC REHABILITATION
Smadi, K. A. (2023) · Journal of Southwest Jiaotong University · doi:10.35741/issn.0258-2724.58.2.53 · Cited by 1
Robotic Exoskeleton With Mechanically Implemented Kinematic Synergy for Quadrupedal Gait of Rats
Miyamoto, T., Mikhailov, A., Hassan, M., Puentes, S., Hiraga, T., Soya, H., & Suzuki, K. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3537858 · Cited by 1
Lora-Millán, J. S., Ramos, J., Carballeira, J., Castano, J. A., & del‐Ama, A. J. (2025) · IEEE Access · doi:10.1109/access.2025.3587807 · Cited by 1
González-Mejía, S., & Ramírez-Scarpetta, J. M. (2025) · Tecnura · doi:10.14483/22487638.23473 · Cited by 1
Suhaimi, S. A. F., Suhaimi, N. M., & Ramli, M. H. M. (2021) · Mekatronika · doi:10.15282/mekatronika.v3i2.7160 · Cited by 1
Caregiver Robots in Nephrology: Is It Feasible?
Robazzi, M. L. D. C. C., Daniel, A. C. Q. G., Silva, C. A. D. O., Cirillo, C. G., Veiga, E. V., Veiga, J. P. R., Sacramento, L. M., Scandiuzzi, L. A. A., Moreira, M. A. S. P., Sudré, M. R. S., et al. (2022) · Springer eBooks · doi:10.1007/978-3-031-11570-7_26 · Cited by 1
IMU-based continuous prediction of human lower limb joint angles
Liu, Z., Wei, J., Xing, Y., Song, J., & Zhang, J. (2024) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma61710.2024.10632997 · Cited by 1
Minimizing Interference in Robotic Rehabilitation via Asymmetric Stiffness Force Fields
Kato, Y., Sakaino, S., & Tsuji, T. (2025) · IEEE Open Journal of the Industrial Electronics Society · doi:10.1109/ojies.2025.3570789 · Cited by 1
Adaptive Robust Control with Leakage-Type Control Law for Trajectory Tracking of Exoskeleton Robots
Tian, J., Wang, X., Ma, N., & Zhang, Y. (2024) · Advances in Internet of Things · doi:10.4236/ait.2024.143004 · Cited by 1
Hwang, J., You, S. H., Choi, W. J., & Yi, C. (2021) · Physical Therapy Korea · doi:10.12674/ptk.2021.28.3.215 · Cited by 1
Adaptable system for robotic telerehabilitation with serious games
Consoni, L. J. (2017) · doi:10.11606/d.18.2017.tde-15052017-165044 · Cited by 1
Adaptive control for wearable robots in human-centered rehabilitation tasks
Rajasekaran, V. (2015) · doi:10.5821/dissertation-2117-96044 · Cited by 4
Design of a hybrid spherical manipulator for lower limb exoskeleton applications
Sadeqi, S. (2017) · Summit (Simon Fraser University)
A disturbing recovery approach for lower extremity exoskeletons with passive ankle joints
Wei, W., Cheng, L., Zhang, P., Zhao, S., Li, Q., Xia, Y., Wang, W., & Li, J. (2017) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2017.8015878
Thérriault, P.-Y., Lefay, G., Lord, M.-M., & Desaulnier, A. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-96065-4_2
Use of assistive technologies in daily life: A portrait of current knowledge
Thérriault, P.-Y., & Lefay, G. (2018) · Edorium Journal of Disability and Rehabilitation · doi:10.5348/100044d05pt2018ra
Özdemir, F., Koca, T. T., & Oksuz, F. (2019) · Journal of Clinical Medicine of Kazakhstan · doi:10.23950/1812-2892-jcmk-00670
Pan, L., Song, A., Duan, S., & Shi, X. (2019) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881419873236
Gait Event Detection System for the Control of Lower Limb Exoskeleton
Kalathe, M., Agarwal, S., Sampaath, V., & Daniel, J. (2021) · International Journal of Biomedical and Clinical Engineering · doi:10.4018/ijbce.2021070102
Wearable Lower Limb Exoskeletons for paraplegia: A Review
Kapula, P. R., Pasha, I. A., Kumar, B., & Sowmya, V. (2021) · International Conference on Technological Advancements and Innovations (ICTAI) · doi:10.1109/ictai53825.2021.9673413
Assistance Robotics and Biosensors
Torres, F., Úbeda, A., & Puente, S. T. (2018) · MDPI eBooks · doi:10.3390/books978-3-03897-395-9
Research Status and Development Trend of Control System of Children Rehabilitation Robot
郭, 进. (2023) · Software Engineering and Applications · doi:10.12677/sea.2023.123039
Design and Development of Automated Lower Limb Continuous Passive Motion (CPM) Machine
Dakhore, M., Shelke, N., Meshram, S., & Kuhikar, Y. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.62407
González-Mejía, S., & Ramírez-Scarpetta, J. M. (2024) · Research Square · doi:10.21203/rs.3.rs-4602369/v1
Pflegeroboter in der Nephrologie: Ist das machbar?
Robazzi, M. L. D. C. C., Daniel, A. C. Q. G., Silva, C. A. D. O., Cirillo, C. G., Veiga, E. V., Veiga, J. P. R., Sacramento, L. M., Scandiuzzi, L. A. A., Moreira, M. A. S. P., Sudré, M. R. S., et al. (2024) · Springer eBooks · doi:10.1007/978-3-031-65236-3_26
Robot-Assisted Gait Training in Older Patients with Comorbid Conditions: A Pilot Study
Choi, S. I., Lim, S. J., & Kim, N. Y. (2025) · Experimental Aging Research · doi:10.1080/0361073x.2025.2459546
Olenšek, A., Zadravec, M., Tomc, M., Mamidi, T. K., Vashista, V., & Matjačić, Z. (2025) · Actuators · doi:10.3390/act14050233
Yao, J., Sado, T., Wang, W., Gao, J., Zhao, Y., Qi, Q., & Mukherjee, M. (2021) · Figshare · doi:10.6084/m9.figshare.14111101
Development of Learning Control Strategies for a Cable-Driven Device Assisting a Human Joint
Xiong, H. (2019) · Purdue e-Pubs (Purdue University System) · doi:10.25394/pgs.10791269
Duraiarasu, E., Rao, M., RA, B. V., Ananthanarayanan, B., P, P., & Ajay Kumar, K. (2025) · IEEE East-West Design & Test Symposium (EWDTS) · doi:10.1109/ewdts67441.2025.11303700
Knowledge Frameworks in Lower Limb Rehabilitation Robots
Luu, D., & Nguyen, T. K. (2026) · Journal of Robotics · doi:10.1155/joro/4628010
Xiong, B., Guo, Y., Gan, Z., Deng, Y., Zhang, J., & Zeng, N. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3667747
Ruopp, T., Schmit, B. D., & Schindler-Ivens, S. (2026) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-026-01934-7
RAJENDRAN, N., George, S. T., Krushnakumar, K. P., Ashwala, R. T., & Pandian, S. M. S. (2026) · Open Computer Science · doi:10.1515/comp-2025-0048
Miyamoto, T., Mikhailov, A., Hiraga, T., Hassan, M., Puentes, S., Soya, H., & Suzuki, K. (2026) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2026.3695384
Dynamic feedback control strategies in robot-assisted vocal training
Xu, B. (2026) · Discover Internet of Things · doi:10.1007/s43926-026-00400-z
Zhao, Z., Xu, C., Li, X., Tan, Z., & Zhang, J. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1878057
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
