Classification of Simultaneous Movements Using Surface EMG Pattern Recognition
TLDR
The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements.Abstract:
Advanced upper limb prostheses capable of actuating multiple degrees of freedom (DOFs) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one DOF at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using nonamputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for nonamputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less ( p <; 0.05) than a single linear discriminant analysis (LDA) classifier or a parallel approach. For three-DOF classification, the conditional parallel approach had error rates of 6.6% on discrete and 10.9% on combined motions, while the single LDA had error rates of 9.4% on discrete and 14.1% on combined motions. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements.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.
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Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands
TL;DR: The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods, and show that several factors can be fundamental for the analysis of sEMG data.
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Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control
Janne M. Hahne,F. Biebmann,Ning Jiang,Hubertus Rehbaum,Dario Farina,Frank C. Meinecke,Klaus-Robert Müller,Lucas C. Parra +7 more
TL;DR: This study systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF and showed that KRR, a nonparametric statistical learning method, outperformed the other methods.
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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.
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Intuitive, Online, Simultaneous, and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees
TL;DR: An approach based on the nonnegative matrix factorization of the wrist muscle activation to extract low-dimensional control signals translated by the user into kinematic variables has the potential of providing intuitive and dexterous control of artificial limbs for amputees.
References
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