Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.

By Erik J. Scheme, Kevin B. Englehart. Journal of rehabilitation research and development, 2011

966 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/ca42211f3044b50f1b01647044f85bd674e29438

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Autonomy in Rehabilitation Robotics: An Intersection (2018, neutral): “For a in-depth review of the area of robotic prostheses, we refer the reader to survey articles on prosthesis use (18) and design (21), and myoelectric control (22).”
Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network (2017, methodology): “However, sEMG is non-stationary and sensitive to many factors, such as electrode placement, signal crosstalk and recording environment (Scheme and Englehart, 2011).”
Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands (2016, background): “Pattern recognition algorithms are used to classify the movement that the subject aims to perform according to a label (Scheme and Englehart, 2011).”
Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation (2019, background): “Hence, it a ﬀ ects the EMG-PR control of prosthesis [24].”
Surface Electromyography Signal Processing and Classification Techniques (2013, background): “Feature extraction step in the classification system increase information density of the signal [94].”
Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors (2018, background): “EMG pattern recognition systems generally consist of four inter-connected components: data pre-processing, data windowing, feature extraction, and classiﬁcation [11].”
The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges (2014, methodology): “As alternative to conventional control algorithms implemented in commercial devices, pattern recognition methods have been applied for several decades for classifying EMG signals [10], [11].”