Journal ArticleDOI
EMG feature evaluation for improving myoelectric pattern recognition robustness
Angkoon Phinyomark,Franck Quaine,Sylvie Charbonnier,Christine Serviere,Franck Tarpin-Bernard,Yann Laurillau +5 more
TLDR
This paper proposes and investigates the behavior of fifty time-domain and frequency-domain features to classify ten upper limb motions using electromyographic data recorded during 21days, and shows that sample entropy (SampEn) outperforms other features when compared using linear discriminant analysis (LDA), a robust classifier.Abstract:
In pattern recognition-based myoelectric control, high accuracy for multiple discriminated motions is presented in most of related literature. However, there is a gap between the classification accuracy and the usability of practical applications of myoelectric control, especially the effect of long-term usage. This paper proposes and investigates the behavior of fifty time-domain and frequency-domain features to classify ten upper limb motions using electromyographic data recorded during 21days. The most stable single feature and multiple feature sets are presented with the optimum configuration of myoelectric control, i.e. data segmentation and classifier. The result shows that sample entropy (SampEn) outperforms other features when compared using linear discriminant analysis (LDA), a robust classifier. The averaged test classification accuracy is 93.37%, when trained in only initial first day. It brings only 2.45% decrease compared with retraining schemes. Increasing number of features to four, which consists of SampEn, the fourth order cepstrum coefficients, root mean square and waveform length, increase the classification accuracy to 98.87%. The proposed techniques achieve to maintain the high accuracy without the retraining scheme. Additionally, this continuous classification allows the real-time operation.read more
Citations
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Journal ArticleDOI
The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges
Dario Farina,Ning Jiang,Hubertus Rehbaum,Ales Holobar,Bernhard Graimann,Hans Dietl,Oskar C. Aszmann +6 more
TL;DR: The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness.
Journal ArticleDOI
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Ulysse Côté-Allard,Cheikh Latyr Fall,Alexandre Drouin,Alexandre Campeau-Lecours,Clément Gosselin,Kyrre Glette,François Laviolette,Benoit Gosselin +7 more
TL;DR: The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% and real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.
Journal ArticleDOI
A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions.
Nurhazimah Nazmi,Mohd Azizi Abdul Rahman,Shin-ichiroh Yamamoto,Siti Anom Ahmad,Hairi Zamzuri,Saiful Amri Mazlan +5 more
TL;DR: An overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions is given and various signal analysis methods are compared by illustrating their applicability in real-time settings.
Journal ArticleDOI
Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network.
TL;DR: A self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining is proposed, based on convolutional neural network using short latency dimension-reduced sEMG spectrograms as inputs.
Journal ArticleDOI
Current state of digital signal processing in myoelectric interfaces and related applications
TL;DR: The major benefits and challenges of myoelectric interfaces are evaluated and recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers.
References
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Journal ArticleDOI
Random Forests
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Journal ArticleDOI
Physiological time-series analysis using approximate entropy and sample entropy
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Journal ArticleDOI
A new strategy for multifunction myoelectric control
TL;DR: A novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns is described, which increases the number of functions which can be controlled by a single channel of myOElectric signal but does so in a way which does not increase the effort required by the amputee.
Journal ArticleDOI
Feature reduction and selection for EMG signal classification
TL;DR: In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed and it is indicated that most time domain features are superfluity and redundancy.
Journal ArticleDOI
Myoelectric control systems—A survey
TL;DR: This paper reviews recent research and development in pattern recognition- and non-pattern recognition-based myoelectric control, and presents state-of-the-art achievements in terms of their type, structure, and potential application.
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