Continuous myoelectric control for powered prostheses using hidden Markov models

By Adrian D. C. Chan, Kevin B. Englehart. IEEE Transactions on Biomedical Engineering, 2005

363 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/1086fba91b6c352d865f69807fb7277f4c3ef9f7

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In Biomedical Engineering

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

A Comparison of Surface and Intramuscular Myoelectric Signal Classification (2007, methodology): “These figures were created by merging the results presented in [13] and [15].”
Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion (2011, methodology): “5) Enhanced Majority Vote: A majority vote method was used to postprocess the classification decisions [ 27 ].”
Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb (2008, positive): “Chan and Englehart [16] applied an HMM to discriminate six classes of limb movement based on a four-channel MES.”
Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation (2019, methodology): “Later, in 2005, a promising method by Chan and Englehart [63] added into the row of the continuous controllers.”
Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. (2011, background): “The most popular choices of cla ssifiers in rec ent work are based on marginal advantages in classification performance; these include linear discriminant analysis (LDA) [33], support vector machines [34–37], and hidden Markov models [38–39].”
Design of a cybernetic hand for perception and action (2006, background): “…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 hampered by several limitations.”
Myoelectric control systems - A survey (2007, methodology): “Chan and Englehart [18] showed that a HMM approach provides even better accuracy than MLP; while maintaining intuitive control and a fast response time.”