The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis
Abstract
This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The system was designed with an intuitive configuration interface, similar to existing conventional myoelectric control systems. The system was assessed quantitatively with a classification error metric and functionally with a clothespin test implemented in a virtual environment. For each case, the proposed system was compared to a state-of-the-art pattern recognition system based on linear discriminant analysis and a conventional myoelectric control scheme with mode switching. These assessments showed that the proposed control system had a higher classification error ( p < 0.001) but yielded a more controllable myoelectric control system ( p < 0.001) as measured through a clothespin usability test implemented in a virtual environment. Furthermore, the system was computationally simple and applicable for real-time embedded implementation. This work provides the basis for a clinically viable pattern recognition based myoelectric control system which is robust, easily configured, and highly usable.
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-citationMyoelectric control systems—A survey
Asghari Oskoei, M., & Hu, H. (2007) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.07.009 · Cited by 1,244
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-citationGraupe, D., Salahi, J., & Kohn, K. H. (1982) · Journal of Biomedical Engineering · doi:10.1016/0141-5425(82)90021-8 · Cited by 205 · cited 1×
“Pattern recognition systems operate on the assumption that the set of features describing the myoelectric signal at a given electrode location will be repeatable for a given state of muscle activation and different between activation states [ 5 ].”
Background
Cited by
Farina, D., Jiang, N., Rehbaum, H., Holobar, A., Graimann, B., Dietl, H., & Aszmann, O. C. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2305111 · Cited by 992
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
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-citationA 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
Metadata
- Authors
- Hargrove, L. J., Scheme, E. J., Englehart, K. B., & Hudgins, B. S.
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2010-02
- Volume
- 18
- Issue
- 1
- Pages
- 49-57
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2097823586
- Cited by
- 219
- References
- 4
- FWCI
- 10.92
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis. https://app.cochat.ai/papers/10.1109/tnsre.2009.2039590
- The paper
- Hargrove, L. J., Scheme, E. J., Englehart, K. B., & Hudgins, B. S. (2010). Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/tnsre.2009.2039590
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.
