The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Novel Gait Adaptation and Neuromotor Training Results Using an Active Leg Exoskeleton
Abstract
The gait of every adult is unique and expected to be ingrained within the neuromuscular system. The major scientific question that we ask in this paper if it is possible to alter the gait of healthy individuals using special purpose design of robots and training paradigms. This paper describes novel experimental results with an active leg exoskeleton (ALEX) and a force-field controller (FFC) developed for neuromotor training of gait and rehabilitation of patients with walking disabilities. ALEX is a motorized leg orthosis having a total of 7 DOFs with hip and knee actuated in the sagittal plane. The FFC applies forces on the foot to help the leg move on a desired trajectory. The interaction forces between the subject and the orthosis are designed to be ¿assist-as-needed¿ for safe and effective gait training. Simulations and experimental results with the FFC are presented. Experiments have been performed on six healthy subjects walking on a treadmill. It was shown that a healthy subject could be retrained in about 45 min with ALEX to walk on a treadmill with a considerably altered gait. In the coming months, this powered orthosis will be used for gait training of stroke patients.
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“The effectiveness of ALEX has been demonstrated through clinical trials with stroke survivors [28], [29].”
From Recent developments and challenges of lower extremity exoskeletons (2016) · cites this paper as [29]
In the citing paper
“After training with ALEX, the patients' gait pattern are closer to a healthy gait pattern with increased gait size and walking speeds.”
From Recent developments and challenges of lower extremity exoskeletons (2016) · cites this paper as [29]
Citations
Cites
Armstrong-Hélouvry, B., Dupont, P. E., & de Wit, C. C. (1994) · Automatica · doi:10.1016/0005-1098(94)90209-7 · Cited by 2,683
O’Rourke, J., & Mallinckrodt, A. J. (1995) · Computers in Physics · doi:10.1063/1.4823371 · Cited by 1,202
Treadmill training of paraplegic patients using a robotic orthosis.
Colombo, G., Joerg, M., Schreier, R., & Dietz, V. (2000) · PubMed · Cited by 1,114
Robot assisted gait training with Active Leg Exoskeleton (ALEX)
Banala, S. K., Kim, S. H., Agrawal, S. K., & Scholz, J. P. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.2008280 · Cited by 751
Patient- cooperative strategies for robot-aided treadmill training: first experimental results
Riener, R., Lunenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., & Dietz, V. (2005) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2005.848628 · Cited by 729
Motor learning elicited by voluntary drive
Lotze, M. (2003) · Brain · doi:10.1093/brain/awg079 · Cited by 691
Veneman, J. F., Ekkelenkamp, R., Kruidhof, R., van der Helm, F. C. T., & van der Kooij, H. (2006) · The International Journal of Robotics Research · doi:10.1177/0278364906063829 · Cited by 428
Husemann, B., Müller, F., Krewer, C., Heller, S., & Koenig, E. (2007) · Stroke · doi:10.1161/01.str.0000254607.48765.cb · Cited by 418
A mechanized gait trainer for restoration of gait.
Hesse, S., & Uhlenbrock, D. (2001) · PubMed · Cited by 390
Power assist method based on Phase Sequence and muscle force condition for HAL
Kawamoto, H., & Sankai, Y. (2005) · Advanced Robotics · doi:10.1163/1568553054455103 · Cited by 376
Research works printed but not linked: 2
- perry, 1989, Observational Gait Analysis Handbook
- mankala, 2009, novel swing-assist unmotorized exoskeletons for gait training, J Neuro Eng Rehabil, 6
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Design and control for a gait rehabilitation robot
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(2018) · doi:10.17758/eirai1.f0318108 · Cited by 1
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Zhang, Y., Xu, L., Jin, H., Liang, X., Liu, J., Yan, W., Zhang, W., & Zhou, J. H. (2022) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm54641.2022.9959646 · Cited by 1
Luu, T. P. (2014) · doi:10.32657/10356/58912 · Cited by 1
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Fettrow, T., DiBianca, S. J., dos Santos, F. V., Reimann, H., & Jeka, J. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.28.970152 · Cited by 1
Design and implementation of a gait rehabilitation robot manipulator
Mullins, F. (2011) · doi:10.22215/etd/2011-09302 · Cited by 1
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Yang, E. C.-Y., & Wu, L.-H. (2012) · Journal of Applied Sciences · doi:10.3923/jas.2012.2351.2359
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Nakka, S., Sen, R., & Vashista, V. (2021) · Advances in Robotics - International Conference of The Robotics Society (AIR) · doi:10.1145/3478586.3480646
Guo, J., & Lee, K.-M. (2018) · Research on intelligent manufacturing · doi:10.1007/978-981-13-2667-7_1
AlArfaj, A. A., Mahmoud, H. A. H., & Hafez, A. M. (2022) · Behavioral Sciences · doi:10.3390/bs12080285
Qiu, J., & Liao, K. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.10.069
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Chen, X., Yuan, D., Qin, X., Liu, J., Liu, M., Zhao, L., Li, Y., Huang, S., Guo, Y., & Li, B. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-6486-4_40
Robotics and exoskeletons: Are we close to daily clinical implementation?
Gandolfi, M., & Salaorni, F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99494-1.00004-6
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Aranganathan, V., Nadar, G. C., Vinjay, G. S., Kandhasamy, S., Govindhan, A., & Srinivas, L. (2024) · AIP conference proceedings · doi:10.1063/5.0216192
Hobbs, B., & Artemiadis, P. (2024) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob60516.2024.10719767
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Otálora, S., Moreno, H. A., Múnera, M., & Cifuentes, C. A. (2024) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob60516.2024.10719822
Qi, K., Dai, L., Xu, H., Li, Z., Huang, H., & Nie, Z. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-0792-1_27
Zhou, X., Wu, H., & Chen, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3567651
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Metadata
- Authors
- Banala, S. K., Agrawal, S. K., Kim, S. H., & Scholz, J. P.
- Affiliations
- University of Delaware (US)
- University of South Florida (US)
- ORCID
- Agrawal, S.K.
- Venue
- IEEE/ASME Transactions on Mechatronics
- Published
- 2010-04
- Volume
- 15
- Issue
- 2
- Pages
- 216-225
- ISSN
- 1083-4435 (ISSN-L)
- 1941-014X
- OpenAlex
- W2099726059
- Cited by
- 165
- References
- 33
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 11 · 2023: 9 · 2022: 11 · 2021: 12 · 2020: 13 · 2019: 17 · 2018: 14 · 2017: 12 · 2016: 7 · 2015: 13 · 2014: 11 · 2013: 11 · 2012: 6
- FWCI
- 4.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE/ASME Transactions on Mechatronics (publishedVersion)
- The Royal Society of Chemistry’s Journals, Books and Databases (The Royal Society of Chemistry) (submittedVersion)
- Funders
- National Center for Medical Rehabilitation Research
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- gait adaptation, treadmill walking, powered orthosis, stroke rehabilitation, gait rehabilitation, assist-as-needed control, healthy volunteers
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