The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Neuro-Fuzzy Control of a Robotic Exoskeleton With EMG Signals
Abstract
We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however, it is not very easy to predict the user's upper-limb motion (elbow and shoulder motion) based on the EMG signals in real-time because of the difficulty in using the EMG signals as the controller input signals. In this paper, we propose a robotic exoskeleton for human upper-limb motion assist, a hierarchical neuro-fuzzy controller for the robotic exoskeleton, and its adaptation method.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A new strategy for multifunction myoelectric control
Hudgins, B., Parker, P., & Scott, R. N. (1993) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.204774 · Cited by 2,149
Neuro-fuzzy rule generation: survey in soft computing framework
Mitra, S., & Hayashi, Y. (2000) · IEEE Transactions on Neural Networks · doi:10.1109/72.846746 · Cited by 726
Time-varying stiffness of human elbow joint during cyclic voluntary movement
Bennett, D. J., Hollerbach, J. M., Xu, Y., & Hunter, I. W. (1992) · Experimental Brain Research · doi:10.1007/bf02259118 · Cited by 480
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
Wu, S., Er, M. J., & Gao, Y. (2001) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.940970 · Cited by 389
Variation of muscle moment arms with elbow and forearm position
Murray, W. M., Delp, S. L., & Buchanan, T. S. (1995) · Journal of Biomechanics · doi:10.1016/0021-9290(94)00114-j · Cited by 385
Biomechanics and Neural Control of Posture and Movement
(2000) · Springer eBooks · doi:10.1007/978-1-4612-2104-3 · Cited by 289
An exoskeletal robot for human shoulder joint motion assist
Kiguchi, K., Iwami, K., Yasuda, M., Watanabe, K., & Fukuda, T. (2003) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2003.809168 · Cited by 191
Self-citationAu, A. T. C., & Kirsch, R. F. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.895950 · Cited by 182
An exoskeletal robot for human elbow motion support‐sensor fusion, adaptation, and control
Kiguchi, K., Kariya, S., Watanabe, K., Izumi, K., & Fukuda, T. (2001) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/3477.931520 · Cited by 159
Self-citationIdentification of Complex Systems Based on Neural and Takagi–Sugeno Fuzzy Model
Kukolj, D., & LEVI, E. (2004) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2003.811119 · Cited by 159
EMG-based human-robot interface for rehabilitation aid
Fukuda, O., Tsuji, T., Ohtsuka, A., & Kaneko, M. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1998.680978 · Cited by 93
EMG prosthetic hand controller using real-time learning method
Nishikawa, D., Yu, W., Yokoi, H., & Kakazu, Y. (2003) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1999.814077 · Cited by 91
Fatigue compensation of the electromyographic signal for prosthetic control and force estimation
Park, E., & Meek, S. G. (1993) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.247800 · Cited by 79
An exoskeleton for human elbow and forearm motion assist
Kiguchi, K., Esaki, R., Tsuruta, T., Watanabe, K., & Fukuda, T. (2004) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2003.1249714 · Cited by 50
Self-citationConstructing neuro-fuzzy systems with TSK fuzzy rules and hybrid SVD-based learning
Lee, W.-J., Ouyang, C., & Lee, S.-J. (2003) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz.2002.1006670 · Cited by 11
Suryanarayanan, S. (1996) · Cited by 10
Cited by
A survey on industrial applications of fuzzy control
Precup, R.-E., & Hellendoorn, H. (2011) · Computers in Industry · doi:10.1016/j.compind.2010.10.001 · Cited by 532
An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot
Kiguchi, K., & Hayashi, Y. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2012.2185843 · Cited by 531
Self-citationNEUROExos: a powered elbow exoskeleton for physical rehabilitation
Vitiello, N., Lenzi, T., Roccella, S., De Rossi, S. M. M., Cattin, E., Giovacchini, F., Vecchi, F., & Carrozza, M. C. (2013) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2211492 · Cited by 272
A survey of sensor fusion methods in wearable robotics
Novak, D., & Riener, R. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.08.012 · Cited by 252
EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton
Yin, Y. H., Fan, Y. J., & Xu, L. D. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2011.2178034 · Cited by 197
Metadata
- Authors
- Kiguchi, K., Tanaka, T., & Fukuda, T.
- Venue
- IEEE Transactions on Fuzzy Systems
- Published
- 2004-08
- Volume
- 12
- Issue
- 4
- Pages
- 481-490
- ISSN
- 1063-6706 (ISSN-L)
- OpenAlex
- W2108437236
- Cited by
- 364
- References
- 16
- FWCI
- 11.61
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
