The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Comparison of Surface and Intramuscular Myoelectric Signal Classification
Abstract
The surface myoelectric signal (MES) has been used as an input to controllers for powered prostheses for many years. As a result of recent technological advances it is reasonable to assume that there will soon be implantable myoelectric sensors which will enable the internal MES to be used as input to these controllers. An internal MES measurement should have less muscular crosstalk allowing for more independent control sites. However, it remains unclear if this benefit outweighs the loss of the more global information contained in the surface MES. This paper compares the classification accuracy of six pattern recognition-based myoelectric controllers which use multi-channel surface MES as inputs to the same controllers which use multi-channel intramuscular MES as inputs. An experiment was designed during which surface and intramuscular MES were collected simultaneously for 10 different classes of isometric contraction. There was no significant difference in classification accuracy as a result of using the intramuscular MES measurement technique when compared to the surface MES measurement technique. Impressive classification accuracy (97%) could be achieved by optimally selecting only three channels of surface MES.
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 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
Self-citationGraupe, D., Salahi, J., & Kohn, K. H. (1982) · Journal of Biomedical Engineering · doi:10.1016/0141-5425(82)90021-8 · Cited by 205
Cited by
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-citationA Strategy for Identifying Locomotion Modes Using Surface Electromyography
Huang, H., Kuiken, T. A., & Lipschutz, R. D. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2003293 · Cited by 512
Myoelectric forearm prostheses: State of the art from a user-centered perspective
Peerdeman, B., Boere, D., Witteveen, H., Huis in `tVeld, R., Hermens, H., Stramigioli, S., Rietman, H., Veltink, P., & Misra, S. (2011) · The Journal of Rehabilitation Research and Development · doi:10.1682/jrrd.2010.08.0161 · Cited by 469
Quantifying 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
Control of Hand Prostheses Using Peripheral Information
Micera, S., Carpaneto, J., & Raspopovic, S. (2010) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2010.2085429 · Cited by 403
Zhai, X., Jelfs, B., Chan, R. H. M., & Tin, C. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00379 · Cited by 357
Decoding of Individuated Finger Movements Using Surface Electromyography
Tenore, F. V. G., Ramos, A., Fahmy, A., Acharya, S., Etienne-Cummings, R., & Thakor, N. V. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2005485 · Cited by 342
Classification of Simultaneous Movements Using Surface EMG Pattern Recognition
Young, A. J., Smith, L. H., Rouse, E. J., & Hargrove, L. J. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2232293 · Cited by 320
Self-citationYoung, A. J., Hargrove, L. J., & Kuiken, T. A. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2159216 · Cited by 287
Self-citationOrtiz-Catalan, M., Brånemark, R., & Håkansson, B. (2013) · Source Code for Biology and Medicine · doi:10.1186/1751-0473-8-11 · Cited by 232
Decoding a New Neural–Machine Interface for Control of Artificial Limbs
Zhou, P., Lowery, M. M., Englehart, K. B., Huang, H., Li, G., Hargrove, L., Dewald, J. P. A., & Kuiken, T. A. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00178.2007 · Cited by 215
Self-citationTargeted Muscle Reinnervation and Advanced Prosthetic Arms
Cheesborough, J., Smith, L., Kuiken, T., & Dumanian, G. (2015) · Seminars in Plastic Surgery · doi:10.1055/s-0035-1544166 · Cited by 206
Samuel, O. W., Asogbon, M. G., Geng, Y., Al-Timemy, A. H., Pirbhulal, S., Ji, N., Chen, S., Fang, P., & Li, G. (2019) · IEEE Access · doi:10.1109/access.2019.2891350 · Cited by 185
Ortiz-Catalan, M., Brånemark, R., Håkansson, B., & Delbeke, J. (2012) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-11-33 · Cited by 144
Control of prosthetic hands via the peripheral nervous system
Ciancio, A. L., Cordella, F., Barone, R., Romeo, R. A., Bellingegni, A. D., Sacchetti, R., Davalli, A., Di Pino, G., Ranieri, F., Di Lazzaro, V., et al. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00116 · Cited by 131
A review of current state-of-the-art control methods for lower-limb powered prostheses
Gehlhar, R., Tucker, M., Young, A. J., & Ames, A. D. (2023) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2023.03.003 · Cited by 131
Mastinu, E., Clemente, F., Sassu, P., Aszmann, O., Brånemark, R., Håkansson, B., Controzzi, M., Cipriani, C., & Ortiz-Catalan, M. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0511-2 · Cited by 87
Evaluating EMG feature and classifier selection for application to partial-hand prosthesis control
Adewuyi, A. A., Hargrove, L. J., & Kuiken, T. A. (2016) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2016.00015 · Cited by 85
Self-citationTriwiyanto, T., Pawana, I. P. A., & Purnomo, M. H. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2999505 · Cited by 67
Motor-commands decoding using peripheral nerve signals: a review
Hong, K.-S., Aziz, N., & Ghafoor, U. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab383 · Cited by 63
Classifying the intent of novel users during human locomotion using powered lower limb prostheses
Young, A. J., Simon, A. M., Fey, N. P., & Hargrove, L. J. (2013) · 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) · doi:10.1109/ner.2013.6695934 · Cited by 55
Self-citationFully Implantable Multi-Channel Measurement System for Acquisition of Muscle Activity
Lewis, S., Russold, M., Dietl, H., Ruff, R., Audi, J. M. C., Hoffmann, K.-P., Abu-Saleh, L., Schroeder, D., Krautschneider, W. H., Westendorff, S., et al. (2013) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2253992 · Cited by 52
Jiralerspong, T., Nakanishi, E., Liu, C., & Ishikawa, J. (2017) · Applied Sciences · doi:10.3390/app7111163 · Cited by 30
Lukyanenko, P., Dewald, H. A., Lambrecht, J., Kirsch, R. F., Tyler, D. J., & Williams, M. R. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00833-3 · Cited by 27
Use of probabilistic weights to enhance linear regression myoelectric control
Smith, L. H., Kuiken, T. A., & Hargrove, L. J. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/6/066030 · Cited by 15
Self-citation
Metadata
- Authors
- Hargrove, L. J., Englehart, K., & Hudgins, B.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2007-05
- Volume
- 54
- Issue
- 5
- Pages
- 847-853
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2133530044
- Cited by
- 518
- References
- 2
- FWCI
- 13.94
- 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.