A wavelet-based continuous classification scheme for multifunction myoelectric control

By Kevin B. Englehart, B. Hudgins, Philip A. Parker. IEEE Transactions on Biomedical Engineering, 2001

743 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/6d3143b8f99b8358c3c1484031c7b705413b0418

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses (2005, methodology): “For continuous MES classification, it has been demonstrated that there is no advantage in using time-frequency methods (which have been shown to be powerful in transient MES classification [13], [9]).”
Myoelectric control of prosthetic hands: state-of-the-art review (2016, neutral): “64 Several methods of FP such as principal component analysis (PCA), 32,65–67 a linear–nonlinear FP composed of PCA and a self-organizing feature map, 68 simple Fisher linear discriminant analysis (LDA), 69 LDA-based FP, 63 uncorrelated LDA, 70 combination of Fisher LDA, fuzzy logic, and…”
EMG feature evaluation for improving myoelectric pattern recognition robustness (2013, methodology): “In addition to the robust property of LDA, this classifier was chosen based on several reasons (Englehart et al., 2001; Jain et al., 2012; Kaufmann et al., 2010), i.e. it is a simple statistical approach and does not require any parameter adjustment.”
Decoding of Individuated Finger Movements Using Surface Electromyography (2009, methodology): “Englehart et al. [9], [ 24 ] use a wavelet-based approach as opposed to a time-domain approach to decode movements in real time foregoing the need for a rest state in between events.”
Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation (2019, methodology): “Some of the recently developed features are Wavelet packet transform (WPT) based features [11], short-time Fourier transform (STFT) [39] and EMG synergies by matrix factorization analysis [40].”
A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors (2011, methodology): “For over three decades, EMG has been used as means for amputees to use residual muscles to control upper limbed prostheses [5], [18], [19].”
A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand (2006, methodology): “Nevertheless, WPT features are superior to time and frequency features, such as EMG amplitude and Fourier transform coefficients because the wavelet functions resemble the motor unit action potentials that constitute the gross EMG signal [10], and the wavelet transform represents the EMG signal by t”
Surface Electromyography Signal Processing and Classification Techniques (2013, background): “Wavelet-based feature set reduced in dimension by principal components analysis greatly improves the classification accuracy in myoelectric-controlled prosthesis applicaion [91].”
Feature reduction and selection for EMG signal classification (2012, methodology): “Sample length of the EMG data is set to 256 ms during beginning of the movement because this span contains information of movement data (Graupe & Cline, 1975), and the response time for the prosthetic limb control should be less than 300 ms in order to reach the real-time constraint (Englehart et al”
Myoelectric forearm prostheses: state of the art from a user-centered perspective. (2011, neutral): “[25] 2005 GMM RMS, AR6, TD set (4) Nondisabled (12) 256 6 4 97”
Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb (2008, positive): “The following experiment compares SVM with two well-known classifiers, including LDA and MLP neural networks.”
A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions (2016, methodology): “Two years later, a LDA classiﬁer performs well than a MLP classiﬁer for the TFD based features sets [36] as mentioned in [44].”
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, methodology): “Waveletbased features have been used in the past for sEMG-based hand gesture recognition [22].”