The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Lower Limb Wearable Robots for Assistance and Rehabilitation: A State of the Art
Abstract
Neurologic injuries, such as stroke, spinal cord injuries, and weaknesses of skeletal muscles with elderly people, may considerably limit the ability of this population to achieve the main daily living activities. Recently, there has been an increasing interest in the development of wearable devices, the so-called exoskeletons, to assist elderly as well as patients with limb pathologies, for movement assistance and rehabilitation. In this paper, we review and discuss the state of the art of the lower limb exoskeletons that are mainly used for physical movement assistance and rehabilitation. An overview of the commonly used actuation systems is presented. According to different case studies, a classification and comparison between different types of actuators is conducted, such as hydraulic actuators, electrical motors, series elastic actuators, and artificial pneumatic muscles. Additionally, the mainly used control strategies in lower limb exoskeletons are classified and reviewed, based on three types of human-robot interfaces: the signals collected from the human body, the interaction forces between the exoskeleton and the wearer, and the signals collected from exoskeletons. Furthermore, the performances of several typical lower limb exoskeletons are discussed, and some assessment methods and performance criteria are reviewed. Finally, a discussion of the major advances that have been made, some research directions, and future challenges are presented.
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Cites
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Brain–machine interfaces: past, present and future
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Haines, C. S., Lima, M. D., Li, N., Spinks, G. M., Foroughi, J., Madden, J. D. W., Kim, S. H., Fang, S., Jung de Andrade, M., Göktepe, F., et al. (2014) · Science · doi:10.1126/science.1246906 · Cited by 1,350
Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art
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Review of control strategies for robotic movement training after neurologic injury
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Cited by
Wang, C., He, T., Zhou, H., Zhang, Z., & Lee, C. (2023) · Bioelectronic Medicine · doi:10.1186/s42234-023-00118-1 · Cited by 295
GAVRe2: Towards Data-Driven Upper-Limb Rehabilitation with Adaptive-Feedback Gamification
Lai, Y., Sutjipto, S., Clout, M. D., Carmichael, M. G., & Paul, G. (2018) · 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2018.8665105 · Cited by 13
Perception of Society’s Trust in Care Robots by Public Opinion Leaders
Hoppe, J. A., Melkas, H., Pekkarinen, S., Tuisku, O., Hennala, L., Johansson-Pajala, R.-M., Gustafsson, C., & Thommes, K. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2081283 · Cited by 7
Metadata
- Authors
- Huo, W., Mohammed, S., Moreno, J. C., & Amirat, Y.
- Venue
- IEEE Systems Journal
- Published
- 2016-09
- Volume
- 10
- Issue
- 3
- Pages
- 1068-1081
- ISSN
- 1932-8184 (ISSN-L)
- 1937-9234
- 2373-7816
- OpenAlex
- W2326141327
- Cited by
- 455
- References
- 78
- FWCI
- 7.61
- Type
- journal article
- Language
- en
- Open access
- closed
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