Literature DB >> 17045489

Myoelectric signal processing for control of powered limb prostheses.

P Parker1, K Englehart, B Hudgins.   

Abstract

Progress in myoelectric control technology has over the years been incremental, due in part to the alternating focus of the R&D between control methodology and device hardware. The technology has over the past 50 years or so moved from single muscle control of a single prosthesis function to muscle group activity control of multifunction prostheses. Central to these changes have been developments in the means of extracting information from the myoelectric signal. This paper gives an overview of the myoelectric signal processing challenge, a brief look at the challenge from an historical perspective, the state-of-the-art in myoelectric signal processing for prosthesis control, and an indication of where this field is heading. The paper demonstrates that considerable progress has been made in providing clients with useful and reliable myoelectric communication channels, and that exciting work and developments are on the horizon.

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Mesh:

Year:  2006        PMID: 17045489     DOI: 10.1016/j.jelekin.2006.08.006

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  89 in total

1.  Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration.

Authors:  Aaron J Young; Levi J Hargrove; Todd A Kuiken
Journal:  IEEE Trans Biomed Eng       Date:  2011-11-29       Impact factor: 4.538

2.  Comparison of electromyography and force as interfaces for prosthetic control.

Authors:  Elaine A Corbett; Eric J Perreault; Todd A Kuiken
Journal:  J Rehabil Res Dev       Date:  2011

3.  An electrooptical muscle contraction sensor.

Authors:  Alessio Chianura; Mario E Giardini
Journal:  Med Biol Eng Comput       Date:  2010-05-21       Impact factor: 2.602

Review 4.  The evolution of functional hand replacement: From iron prostheses to hand transplantation.

Authors:  Kevin J Zuo; Jaret L Olson
Journal:  Plast Surg (Oakv)       Date:  2014       Impact factor: 0.947

5.  The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift.

Authors:  Aaron J Young; Levi J Hargrove; Todd A Kuiken
Journal:  IEEE Trans Biomed Eng       Date:  2011-06-09       Impact factor: 4.538

6.  A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury.

Authors:  Jie Liu; Ping Zhou
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2012-09-27       Impact factor: 3.802

7.  A Training Strategy for Learning Pattern Recognition Control for Myoelectric Prostheses.

Authors:  Michael A Powell; Nitish V Thakor
Journal:  J Prosthet Orthot       Date:  2013-01-01

8.  Recognition of handwriting from electromyography.

Authors:  Michael Linderman; Mikhail A Lebedev; Joseph S Erlichman
Journal:  PLoS One       Date:  2009-08-26       Impact factor: 3.240

9.  The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements.

Authors:  Natasha Alves; Tom Chau
Journal:  J Neuroeng Rehabil       Date:  2010-05-21       Impact factor: 4.262

10.  A pneumatically powered knee-ankle-foot orthosis (KAFO) with myoelectric activation and inhibition.

Authors:  Gregory S Sawicki; Daniel P Ferris
Journal:  J Neuroeng Rehabil       Date:  2009-06-23       Impact factor: 4.262

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