Classification of Simultaneous Movements using Surface EMG Pattern Recognition

By Aaron J. Young, Lauren H. Smith, Elliott J. Rouse, Levi J. Hargrove. IEEE Transactions on Biomedical Engineering, 2012

306 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/9ac4c2410354e01f88bc142af8eb290529874561

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Citations in the corpus

Autonomy in Rehabilitation Robotics: An Intersection (2018, unknown): “Therefore, a large body of pattern-recognition research focuses on advancing EMG decoding paradigms—evaluating a variety, even cascades (40), of machine learning algorithms (41, 42) and different input features for these algorithms (41, 42, 43), as well as exploring different output classes (44, 45,”
The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges (2014, background): “Recently, there have been efforts toward simultaneous control of multiple classes within the pattern recognition paradigm [69]–[72].”
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, background): “Simultaneous pattern recognition has been studied recently (Jiang et al., 2013; Ortiz-Catalan et al., 2013; Young et al., 2013).”
Myoelectric control of prosthetic hands: state-of-the-art review (2016, unknown): “Researchers are now attempting to simultaneously control using pattern recognition method.(103,104)”