A robust, real-time control scheme for multifunction myoelectric control

By Kevin B. Englehart, B. Hudgins. IEEE Transactions on Biomedical Engineering, 2003

1,732 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b0f5fa8411129c2ca0925ec4c1fecab37434dc21

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Design of a cybernetic hand for perception and action (2006, neutral): “Even if interesting results have been achieved by several groups extracting motor information (Micera et al. 1999; Englehart and Hudgins 2003; Reischl et al. 2004; Ajiboye and R. Weir 2005; Chan and Englehart 2005; Huang et al. 2005), extracting users’ voluntary intentions is still difficult and ham”
Targeted Muscle Reinnervation for Real-Time Myoelectric Control of Multifunction Artificial Arms (2009, neutral): “The values measured here were similar to previously reported data from our laboratory and from other investigations using pattern recognition of EMG signals to classify intended movements.(16,20,21) However, classification accuracy is not a dynamic estimation of performance but rather only the avera”
A Comparison of Surface and Intramuscular Myoelectric Signal Classification (2007, background): “These decisions were smoothed by determining the majority vote of the decisions comprising the current window, the previous eight windows, and the next eight windows [ 2 ].”
Autonomy in Rehabilitation Robotics: An Intersection (2018, positive): “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,”
A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions (2016, background): “Englehart and Hudgins pointed out that the different lengths of EMG data effect its classiﬁcation error [32].”
Enabling always-available input with muscle-computer interfaces (2009, background): “Previous work exploring muscle-sensing for input has primarily focused either on using a single large muscle (rather than the fingers) [2, 3, 4, 22, 25], which does not provide the breadth of input signals required for computer input, and/or on situations where the hand and arm are constrained to a ”
Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb (2008, neutral): “This work was continued over the last 15 years, by employing various classifiers, such as linear discriminant analysis (LDA) [4], [5], MLP/radial basis function (RBF) neural networks [6], time-delayed artificial neural network (ANN) [7], fuzzy [8], [9], NEUROfuzzy [10], fuzzy ARTMAP networks [11],…”
Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion (2011, background): “[9]–[12], no EMG-controlled lower limb prostheses are commercially available.”
On the Shared Control of an EMG-Controlled Prosthetic Hand: Analysis of User–Prosthesis Interaction (2008, background): “Nevertheless, current smart classifiers do not provide more than six recognized classes (or primitives) [17], [18], and their practical reliability must be evaluated; moreover, their controllability in prostheses application is neither natural nor intuitive.”
Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation (2019, methodology): “Englehart and Hudgins [29] used an adjacent, disjoint analysis window length equivalent to 0.25 × Sample frequency (SF) (256 ms for SF of 1000 Hz) for continuous myoelectric classiﬁcation [30].”
Myoelectric Control of Artificial Limbs¿Is There a Need to Change Focus? [In the Spotlight] (2012, background): “The pattern classification approach for myoelectric control is based on the assumption that there exist distinguishable and repeatable signal patterns among (continued on page 148)”
A Strategy for Identifying Locomotion Modes Using Surface Electromyography (2009, methodology): “The simple LDA was reported as having comparable classification performance to more complex types [20]–[22] and as being computationally efficient for real-time myoelectric prosthesis control [8].”
Classification of Simultaneous Movements using Surface EMG Pattern Recognition (2012, methodology): “frame increment [17], [27] where each 250 ms window was provided to the classifier as a single example for the training procedure.”
A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses (2005, background): “It has been demonstrated that a MV algorithm helps to significantly eliminate spurious misclassifications [15], [14].”