The DOI matches its Crossref record; 174 of the 175 references it cites were found in a registry or an index.
Deep Learning Approaches for Enhanced Lower-Limb Exoskeleton Control: A Review
Open accessAbstract
Recent advancements in robotics have pushed the development of active exoskeletons and orthoses for assistive, augmentative, and rehabilitative purposes. Deep Learning approaches, particularly in motion analysis and prediction, hold promise for enhanced control of lower limb exoskeletons, offering the potential for improved outcomes in assistive and rehabilitative interventions. This review paper explores recent advancements in deep learning approaches for controlling lower limb exoskeletons. The study encompasses papers published from 2018 to the present, focusing on various aspects of deep learning models, including recognition, prediction, and other related tasks. The paper includes 103 papers covering various tasks and parameters like the gait phase, kinematics, and kinetics of the lower limb exoskeleton. Each aspect is thoroughly examined, highlighting the parameters utilized in the respective models. Moreover, the results obtained from these approaches are evaluated and compared against classical control strategies, shedding light on their effectiveness and potential benefits. The review also addresses the limitations of current deep learning techniques in lower limb exoskeleton control and outlines potential avenues for future research and improvement. By consolidating the latest findings and advancements in this field, this review paper provides valuable insights into the application of deep learning in the control of lower limb exoskeletons, paving the way for enhanced rehabilitation and assistive technologies in the future.
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1 retracted
Li, R., Zou, K., & Wang, W. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01993-1 · Cited by 6
RetractedPreferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554
A survey of vision-based methods for action representation, segmentation and recognition
Weinland, D., Ronfard, R. P., & Boyer, E. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.10.002 · Cited by 987
Review the state-of-the-art technologies of semantic segmentation based on deep learning
Mo, Y., Wu, Y. X., Yang, X., Liu, F., & Liao, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.005 · Cited by 669
Gait analysis methods in rehabilitation
Baker, R. J. (2006) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-3-4 · Cited by 633
A Review on Lower Limb Rehabilitation Exoskeleton Robots
Shi, D., Zhang, W., Zhang, W., & Ding, X. (2019) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-019-0389-8 · Cited by 534
Saposnik, G., Cohen, L. G., Mamdani, M., Pooyania, S., Ploughman, M., Cheung, D., Shaw, J., Hall, J., Nord, P., Dukelow, S. P., et al. (2016) · The Lancet Neurology · doi:10.1016/s1474-4422(16)30121-1 · Cited by 430
Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies
Proietti, T., Crocher, V., Roby-Brami, A., & Jarrassé, N. (2016) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2016.2552201 · Cited by 395
Review of control strategies for lower-limb exoskeletons to assist gait
Baud, R., Manzoori, A. R., Ijspeert, A. J., & Bouri, M. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00906-3 · Cited by 394
Gait Partitioning Methods: A Systematic Review
Taborri, J., Palermo, E., Rossi, S., & Cappa, P. (2016) · Sensors · doi:10.3390/s16010066 · Cited by 361
Research works printed but not linked: 6
- 2023, World Health Organization
- 2023, World Stroke Organization
- 2022, The Rewalk Exoskeleton
- 2022, The Lokomat Exoskeleton
- Hasan, 2022, Deep learning technology-based exoskeleton robot controller development
- Ibrahim, 2023, FocalGatedNet: A novel deep learning model for accurate knee joint angle prediction
Cited by
Sorted by citations
AI in Orthopedic Research: A Comprehensive Review
Mısır, A., & Yuce, A. (2025) · Journal of Orthopaedic Research® · doi:10.1002/jor.26109 · Cited by 46
Guerra, B. M. V., Schmid, M., Sozzi, S., Pizzocaro, S., Nunzio, A. M. D., & Ramat, S. (2024) · Sensors · doi:10.3390/s24206666 · Cited by 17
Research on Control Strategy of Lower Limb Exoskeleton Robots: A Review
Xu, X. F., Chen, C., Sun, Z., Xian, W., Ma, L., & Liu, Y. (2026) · Sensors · doi:10.3390/s26020355 · Cited by 12
Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9
Belal, M., Hassan, T., Hassan, A., Alsheikh, N., Elhendawi, N., & Hussain, I. (2024) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta63115.2024.00072 · Cited by 9
Self-citation
Metadata
- Authors
- Belal, M., Alsheikh, N., Aljarah, A., & Hussain, I.
- Affiliations
- Khalifa University of Science and Technology (AE)
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 143883-143907
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4399618574
- Cited by
- 42
- References
- 175
- Citations by year
- 2026: 20 · 2025: 16 · 2024: 6
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Advanced Research and Innovation Center (ARIC), jointly funded by Mubadala, United Arab Emirates (UAE) Clusters, and the Khalifa University of Science and Technology (8436010)
- Khalifa University Center for Autonomous and Robotic Systems (RC1-2018-KUCARS)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- lower limb exoskeleton, exoskeleton control, deep learning, gait phase detection, gait kinematics, gait kinetics, motion prediction, rehabilitation robotics, assistive technology, orthoses
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- Belal, M., Alsheikh, N., Aljarah, A., & Hussain, I. (2024). Deep Learning Approaches for Enhanced Lower-Limb Exoskeleton Control: A Review. IEEE Access. https://doi.org/10.1109/access.2024.3414175
