The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses
Abstract
This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus of this work is to optimize the configuration of this classification scheme. To that end, a complete experimental evaluation of this system is conducted on a 12 subject database. The experiments examine the GMMs algorithmic issues including the model order selection and variance limiting, the segmentation of the data, and various feature sets including time-domain features and autoregressive features. The benefits of postprocessing the results using a majority vote rule are demonstrated. The performance of the GMM is compared to three commonly used classifiers: a linear discriminant analysis, a linear perceptron network, and a multilayer perceptron neural network. The GMM-based limb motion classification system demonstrates exceptional classification accuracy and results in a robust method of motion classification with low computational load.
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-citation
Cited by
Myoelectric 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
Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms
Kuiken, T. A. (2009) · JAMA · doi:10.1001/jama.2009.116 · Cited by 1,089
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-citationConsumer design priorities for upper limb prosthetics
Biddiss, E., Beaton, D., & Chau, T. (2007) · Disability and Rehabilitation: Assistive Technology · doi:10.1080/17483100701714733 · Cited by 519
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
Parajuli, N., Sreenivasan, N., Bifulco, P., Cesarelli, M., Savino, S., Niola, V., Esposito, D., Hamilton, T. J., Naik, G. R., Gunawardana, U., et al. (2019) · Sensors · doi:10.3390/s19204596 · Cited by 332
Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees
Cipriani, C., Antfolk, C., Controzzi, M., Lundborg, G., Rosen, B., Carrozza, M. C., & Sebelius, F. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2108667 · Cited by 233
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-citationSamuel, 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
Xie, H.-B., Zheng, Y.-P., & Guo, J.-Y. (2009) · Physiological Measurement · doi:10.1088/0967-3334/30/5/002 · Cited by 118
Micera, S., Rossini, P. M., Rigosa, J., Citi, L., Carpaneto, J., Raspopovic, S., Tombini, M., Cipriani, C., Assenza, G., Carrozza, M. C., et al. (2011) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-8-53 · Cited by 100
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
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
Jiralerspong, T., Nakanishi, E., Liu, C., & Ishikawa, J. (2017) · Applied Sciences · doi:10.3390/app7111163 · Cited by 30
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
Metadata
- Authors
- Huang, Y., Englehart, K. B., Hudgins, B., & Chan, A. D. C.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2005-11
- Volume
- 52
- Issue
- 11
- Pages
- 1801-1811
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2168162876
- Cited by
- 643
- References
- 1
- FWCI
- 12.29
- 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.
