The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network
Open accessAbstract
Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.
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
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
(2011) · Cited by 6,993
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
MatConvNet: Convolutional Neural Networks for MATLAB
Vedaldi, A., & Lenc, K. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2807412 · Cited by 2,444
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
Cited by
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition
Hu, Y., Wong, Y., Wei, W., Du, Y., Kankanhalli, M., & Geng, W. (2018) · PLOS ONE · doi:10.1371/journal.pone.0206049 · Cited by 398
EMG Pattern Recognition in the Era of Big Data and Deep Learning
Phinyomark, A., & Scheme, E. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2030021 · Cited by 272
Classification of Electromyographic Hand Gesture Signals using Machine Learning Techniques
Jia, G., Lam, H.-K., Liao, J., & Wang, R. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.03.009 · Cited by 89
Challenges in Task Incremental Learning for Assistive Robotics
Feng, F., Chan, R. H. M., Shi, X., Zhang, Y., & She, Q. (2020) · IEEE Access · doi:10.1109/access.2019.2955480 · Cited by 39
Self-citationShen, S., Wang, X., Wu, M., Gu, K., Chen, X., & Geng, X. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3236682 · Cited by 29
Metadata
- Authors
- Zhai, X., Jelfs, B., Chan, R. H. M., & Tin, C.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2017-07-11
- Volume
- 11
- Pages
- 379-379
- ISSN
- 1662-453X (ISSN-L)
- OpenAlex
- W2684229413
- Cited by
- 357
- References
- 41
- FWCI
- 13.73
- Type
- journal article
- Language
- en
- Open access
- gold
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: Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network. https://app.cochat.ai/papers/10.3389/fnins.2017.00379
- The paper
- Zhai, X., Jelfs, B., Chan, R. H. M., & Tin, C. (2017). Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network. Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2017.00379
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.
