The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
A Strategy for Identifying Locomotion Modes Using Surface Electromyography
Open accessAbstract
This study investigated the use of surface electromyography (EMG) combined with pattern recognition (PR) to identify user locomotion modes. Due to the nonstationary characteristics of leg EMG signals during locomotion, a new phase-dependent EMG PR strategy was proposed for classifying the user's locomotion modes. The variables of the system were studied for accurate classification and timely system response. The developed PR system was tested on EMG data collected from eight able-bodied subjects and two subjects with long transfemoral (TF) amputations while they were walking on different terrains or paths. The results showed reliable classification for the seven tested modes. For eight able-bodied subjects, the average classification errors in the four defined phases using ten electrodes located over the muscles above the knee (simulating EMG from the residual limb of a TF amputee) were 12.4% +/- 5.0%, 6.0% +/- 4.7%, 7.5% +/- 5.1%, and 5.2% +/- 3.7%, respectively. Comparable results were also observed in our pilot study on the subjects with TF amputations. The outcome of this investigation could promote the future design of neural-controlled artificial legs.
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
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,370
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in Industrial Control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,862
An introduction to multisensor data fusion
Hall, D. L., & Llinas, J. (1997) · Proceedings of the IEEE · doi:10.1109/5.554205 · Cited by 2,629
Pattern Classification (2nd ed.)
Duda, R. O., Hart, P. E., & Stork, D. G. (1999) · Cited by 2,520
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
A robust, real-time control scheme for multifunction myoelectric control
Englehart, K., & Hudgins, B. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.813539 · Cited by 1,743
Myoelectric signal processing for control of powered limb prostheses
Parker, P., Englehart, K., & Hudgins, B. (2006) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2006.08.006 · Cited by 651
Classification of the myoelectric signal using time-frequency based representations
Englehart, K., Hudgins, B., Parker, P. A., & Stevenson, M. (1999) · Medical Engineering & Physics · doi:10.1016/s1350-4533(99)00066-1 · Cited by 645
Huang, Y., Englehart, K. B., Hudgins, B., & Chan, A. D. C. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.856295 · Cited by 643
Kuiken, T. A., Dumanian, G. A., Lipschutz, R. D., Miller, L. A., & Stubblefield, K. A. (2004) · Prosthetics & Orthotics International · doi:10.3109/03093640409167756 · Cited by 583
Self-citation
Research works printed but not linked: 2
- 10.1007/BF02442284 · doi:10.1007/bf02442284
- 10.1007/BF02474569 · doi:10.1007/bf02474569
Cited by
Control strategies for active lower extremity prosthetics and orthotics: a review
Tucker, M. R., Olivier, J., Pagel, A., Bleuler, H., Bouri, M., Lambercy, O., Millán, J. D. R., Riener, R., Vallery, H., & Gassert, R. (2015) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-12-1 · Cited by 1,047
Huang, H., Zhang, F., Hargrove, L. J., Dou, Z., Rogers, D. R., & Englehart, K. B. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2161671 · Cited by 542
Self-citationQuantifying Pattern Recognition—Based Myoelectric Control of Multifunctional Transradial Prostheses
Li, G., Schultz, A. E., & Kuiken, T. A. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2039619 · Cited by 443
Self-citationMulticlass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis
Varol, H. A., Sup, F., & Goldfarb, M. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2034734 · Cited by 425
Toward higher-performance bionic limbs for wider clinical use
Farina, D., Vujaklija, I., Brånemark, R., Bull, A. M. J., Dietl, H., Graimann, B., Hargrove, L. J., Hoffmann, K.-P., Huang, H., Ingvarsson, T., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00732-x · Cited by 263
Self-citation
Metadata
- Authors
- Huang, H., Kuiken, T. A., & Lipschutz, R. D.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2009-01
- Volume
- 56
- Issue
- 1
- Pages
- 65-73
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2106951868
- Cited by
- 512
- References
- 30
- FWCI
- 8.10
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Strategy for Identifying Locomotion Modes Using Surface Electromyography. https://app.cochat.ai/papers/10.1109/tbme.2008.2003293
- The paper
- Huang, H., Kuiken, T. A., & Lipschutz, R. D. (2009). A Strategy for Identifying Locomotion Modes Using Surface Electromyography. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2008.2003293
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.
